Abstract
This study employs a corpus-assisted ecolinguistic analysis of Bangladeshi media discourse to reveal the frames of climate news. Drawing on a corpus of national English-language newspapers, the analysis examines the collocations and semantic fields of key terms such as climate, finance, adaptation, and loss and damage. By integrating corpus-assisted ecolinguistic approach with framing theory, the study identifies how linguistic choices construct relationships between environment, economy, and national identity. The findings show that climate discourse in Bangladesh is predominantly economized, aligning environmental recovery with financial growth and policy frameworks rather than ecological restoration. Representations of justice and adaptation are often reframed as matters of funding and investment, thereby translating moral and ecological imperatives into administrative and fiscal concerns. The analysis also shows that nonhuman nature has limited agency, as human-centered narratives of governance, progress, and responsibility remain dominate. By uncovering these discursive patterns, the study demonstrates how national media normalizes particular narratives of climate action and sustainability, thereby shaping the moral and political conceptions of environmental responsibility in the Global South.
1 Introduction
For the last two decades, anthropogenic climate change has become an ever-present topic worldwide, demanding urgent attention across diverse contexts. The importance of addressing this issue has steadily increased, as it threatens ecological stability and human livelihoods. Consequently, climate change has become a central concern not only in environmental science and policy but also within the humanities and social sciences, reflecting what scholars have termed an “ecological turn” (He 2021; Penz and Fill 2022; Stibbe 2021; Zhou 2021). Ecolinguistics, an emerging field, has contributed meaningfully to this turn by examining how language shapes human relationships with the natural world and by uncovering the underlying narratives that sustain or endanger ecological well-being (Steffensen 2024, 2025; Stibbe 2021, 2024).
The least responsible countries for climate change are often the most severely affected. Bangladesh exemplifies this paradox. Bangladesh is one of the most climate-vulnerable countries due to its low-lying deltaic topography, dense river networks, and monsoonal climate (Alam et al. 2017). Over recent decades, the country has experienced a mean temperature increase of about 0.20 °C per decade and irregular rainfall patterns, heavier in the monsoon, yet colder and drier in winter, which leads to more frequent and severe weather extremes (Chowdhury et al. 2022). People living in coastal and riverine areas are at great risk due to the limited adaptive resources (Hoque et al. 2019). Approximately 60 % of the coastal population relies on agriculture for their livelihoods, leaving them particularly exposed to salinity intrusion, crop failure, and erosion (Hoque et al. 2019). Sea-level rise of 0.29–1.1 m by the end of the century threatens wetlands, floods, and shoreline erosion, displacing communities and exacerbating gendered vulnerabilities, as men migrate for work and women face heightened social and economic insecurity (Rahman 2013).
The media serve as the most powerful tool in shaping societal understanding of such complex issues (Peters and Heinrichs 2005). As van Dijk (2015) argues, media discourse occupies a frontline role in constructing and negotiating social meaning, partly because of the public’s trust in media credibility and its pervasive influence on everyday life (Fairclough 1989). With the expansion of global ecological problems, news media have not only maintained their function as channels of information but have also become active agents influencing values, attitudes, and lifestyle choices. Stibbe (2021) maintains that the media’s language and storytelling have immense potential to reflect and promote ecological consciousness.
Mass media are particularly crucial for climate discourse because climate change lies largely beyond most individuals’ direct experience or “life-world” (Moser 2010; Neverla and Schäfer 2012). Consequently, public understanding of the issue depends heavily on mediated communication (Anderson 2011; Schäfer 2012). Media coverage thus becomes a central forum for the discussion, legitimation, and even contestation of climate governance (Nanz and Steffek 2004; Schneider et al. 2007; Steffek 2009). By broadcasting the voices of diverse social actors – scientists, policymakers, NGOs, and communities – the media influence how societies perceive climate urgency and responsibility. Moreover, media debates can create circumstances “where it is conducive for governments to act, or hard for them not to act in the face of perceived pressure to initiate a policy response” (Newell 2000: 94).
In Bangladesh, where climate change poses significant risks to livelihoods and food security, the media’s representational practices acquire particular importance. As the country’s vulnerability is well documented, this study investigates the manner in which the print media linguistically represent climate-related threats and opportunities. By examining how language, ideology, and ecology converge in the press, the study contributes to an understanding of media framing in a highly climate-vulnerable context.
Accordingly, this study employs an ecolinguistic approach to examine how Bangladeshi English-language newspapers report on climate change. Drawing on both corpus-assisted and framing analyses, it investigates the most frequent words, expressions, and recurring linguistic patterns in news stories, and considers how these shape broader portrayals of climate crisis. By integrating quantitative word analysis with qualitative interpretation, the study aims to reveal how news narratives construct either anthropocentric (human-centered) or ecocentric (nature-centered) framings of climate change in Bangladesh.
2 Literature review
Ecolinguistics has become increasingly important as a discipline for examining how language interplays with ecological issues (Steffensen 2024, 2025). The term itself came into more common use in the 1990s, although its roots date earlier. Haugen (1972: 325) first conceptualized ecolinguistics as the study of language and its interaction with its environment, especially emphasizing the psychological and sociological settings over mere physical surroundings. Halliday (2001 [1990]) expanded on this by arguing that language, especially English, has features that “construe reality in a certain way” that may no longer be healthy for humans and other species. More recently, the discourse-analytic branch of ecolinguistics has defined its scope to include “the role of language in the life-sustaining interactions of humans, other species, and the physical environment” (https://www.ecolinguistics-association.org/). Although Critical Discourse Studies (CDS) and ecolinguistics share concerns with justice, power, and ideology (Alexander and Stibbe 2014; Stibbe 2014), ecolinguistics moves beyond anthropocentric priorities by extending its commitment to justice and wellbeing to nonhuman animals and ecological systems (Poole and Micalay-Hurtado 2022).
Corpus-assisted ecolinguistic research has leveraged quantitative tools (frequency, collocation, N-grams, concordance) to reveal subtle patterns of meaning, ideology, and framing in ecological discourse (Poole 2022). Much corpus-assisted ecolinguistic research has explored the discourse of climate change (e.g. Gillings and Dayrell 2024), representations of nonhuman animals (e.g. Goatly 2002; Sealey and Oakley 2013) and plants (e.g. Poole and Micalay-Hurtado 2022), political and corporate discursive treatment of the environment (e.g. Collins and Nerlich 2015; Franklin et al. 2022; Fløttum et al. 2014; Koteyko et al. 2010, 2013), as well as studies of ecology-relevant key terms (e.g. Gilquin 2022; Liu and Huang 2022). In the realm of mass media, several corpus-assisted ecolinguistic investigations provide useful precedent. For example, Liu and Huang (2022) analyzed the framing differences between “climate change” and “global warming” in The New York Times. Kramar (2023) examined how leading UK and US news outlets construct agency in climate change discourse. Gillings and Dayrell (2024) investigated discourse fluctuations in UK press coverage from 2003 to 2019. Hambali et al. (2025) analyzed Indonesian print-media coverage and found a strong prevalence of war and threat metaphors regarding climate change, yet noted limited agency attributed to local actors or to nature itself. These studies highlight the role of media language in shaping public understanding of climate crisis.
Framing and salience are crucial for understanding how media shape perceptions of climate change. Entman (1993) defines framing as selecting certain aspects of an issue while excluding others, thereby influencing how audiences notice, interpret, and respond to it. Frames should be made explicit for reflection rather than judged as right or wrong, as several scholars emphasize that framing directs attention to particular causes, problems, or remedies and may embed moral evaluations within these selections (Borah 2011). Salience refers to making information more noticeable, meaningful, or memorable, which increases the likelihood that audiences will perceive, interpret, and retain it (Fiske and Taylor 2013). Importantly, what a frame omits can be as influential as what it includes, as omissions may obscure alternative problem definitions, explanations, or solutions, thereby guiding public understanding and political action (Gamson 1992). By making frames explicit, scholars can critically reflect on the power of discourse to prioritize certain realities over others, a process particularly relevant for analyzing climate change reporting in Bangladeshi newspapers, where linguistic choices may foreground anthropocentric or ecocentric perspectives (Entman 1993; Hulme et al. 2018).
Corpus-assisted ecolinguistic research in Bangladesh remains limited. Sadath et al. (2013) analyzed forest and climate media framing in Bangladeshi and global newspapers from 1989 to 2010 but did not employ corpus-assisted linguistic methods. Miah et al. (2011) examined major climate-change issues in Bangladeshi daily newspapers using thematic content coding. Al-Zaman and Khan (2022) conducted a topic modeling analysis of environmental news in Bangladeshi media from 2013 to 2021, identifying dominant themes such as monsoon impacts and urban environmental issues.
Given the state of existing research, two key gaps emerge in the context of Bangladeshi media: (1) Few studies use corpus-linguistic tools (frequency, collocation, n-gram, concordance) to examine how linguistic choices frame climate change, especially in terms of non-human agency and ecological interdependence. (2) Many studies focus on what topics are covered (themes, issues) rather than how they are linguistically represented (agent-passivity, metaphor, lexical patterns).
This study aims to address both gaps by employing a corpus-assisted ecolinguistic approach to analyze Bangladeshi English-language newspaper articles on climate change. It will explore frequent lexical items, collocation, n-gram patterns, and concordance lines, and interpret framings in terms of anthropocentric and ecocentric perspectives.
3 Theoretical foundations
3.1 Corpus-assisted ecolinguistics
Ecolinguistics is “the study of how language impacts on the natural ecology in ways that change the conditions for life on Earth” (Steffensen 2024: 522). Within the discourse analytic strand of ecolinguistics, language is viewed as both reflecting and shaping human relationships with the natural world (Halliday 2001 [1990]). This approach examines whether discourses perpetuate harmful ecological beliefs (e.g. domination and exploitation) or encourage sustainable practices (e.g. coexistence and respect for non-human agency) based on the analysts’ ecosophy. This study applies Stibbe’s (2021, 2024) ecosophy, “Living!”, as a criteria to evaluate climate narratives in Bangladesh.
Corpus-assisted ecolinguistics “draws from the methodological repertoire of CADS [Corpus-assisted Discourse Studies] and the theoretical frameworks of ecolinguistics and CDS to enable an expanded ecolinguistic research enterprise for the study of language and its impacts, both negative and positive, on well-being and sustainability” (Poole 2022: 24). It attempts to “demonstrate how the complexity of attitudes, beliefs, and ideologies which contribute to climate change, species loss, and ecological degradation are reflected, normalized, and perpetuated in language use” (Poole and Micalay-Hurtado 2022: 2). The corpus-assisted ecolinguistic approach integrates quantitative methods (e.g. keyword analysis, frequency measures, and concordancing analysis) applied on large corpora and qualitative methods used to interpret overall trends identified with quantitative methods (Steffensen 2025). This study employs a corpus-assisted ecolinguistic approach to examine the representation of climate change in Bangladeshi newspapers by analyzing word frequency, collocations, and n-grams.
3.2 Framing theory
Framing theory (Entman 1993; Goffman 1974) provides the interpretive foundation for linking linguistic patterning to meaning construction within the media’s climate discourse. In this study, the theory functions not merely as a conceptual definition but as an analytical tool for identifying how specific discursive selections and emphases structure public understanding of environmental issues. Drawing on Entman’s (1993) notion that framing foregrounds certain aspects of reality while backgrounding others, the analysis examines how recurring linguistic patterns and collocations reproduce particular frames identified by Hulme et al. (2018).
Hulme et al.’s (2018) typology of climate framings such as economic and financial challenge, national security concern, technological opportunity, and moral or ethical dilemma serves as a comparative framework for interpreting concordance patterns emerging from the corpus. Through concordance analyses of sentences and multi-word clusters, the study explores how Bangladeshi news discourse constructs climate change not simply as an environmental phenomenon but as a socio-economic and moral problem. The integration of framing theory with corpus-assisted ecolinguistic approach thus enables a systematic examination of how linguistic choices (e.g. collocates of climate, finance, threat, or responsibility) reflect and reinforce broader ideological orientations in climate communication.
4 Methodology
4.1 Data collection
For the purposes of this research, I assembled an original corpus of 446 climate-related news articles drawn from five leading English-language newspapers in Bangladesh, resulting in 329,045 running words. Following the SCImago Media Rankings, the selected outlets – The Daily Star, Bangladesh News 24, The Financial Express, Dhaka Tribune, and The Daily Sun – represent the country’s top-ranked English newspapers in terms of readership, influence, and journalistic credibility. The key term “climate change” was used as the primary search keyword to retrieve relevant articles. Articles that merely mentioned the term without a substantial discussion of environmental or climatic issues were excluded.
4.2 Corpus building
After data collection, the dataset underwent a manual cleaning process to remove non-linguistic components such as reporter names, dates, advertisements, and hyperlinks. Each text was compiled into a single document file and subsequently uploaded to AntConc (version 4.3.1.0) for analysis. While this approach of combining all articles into a single corpus aligns with the study’s aim to describe national-level discourse rather than inter-outlet variation, it inevitably limits the ability to assess dispersion across publications or diachronic shifts in representation. These aspects are therefore acknowledged as avenues for future research. Table 1 summarizes the distribution of articles and their proportional contributions from each newspaper outlet.
Distribution of articles.
| Newspaper | SCImago rank | Number of articles | Percentage of articles |
|---|---|---|---|
| The Daily Star | 440 | 83 | 18.61 % |
| Bangladesh News 24 | 545 | 92 | 20.63 % |
| The Financial Express | 1,713 | 90 | 20.18 % |
| Dhaka Tribune | 2,079 | 90 | 20.18 % |
| The Daily Sun | 3,047 | 91 | 20.41 % |
4.3 Analytical tools and procedures
The corpus was analyzed using AntConc (version 4.3.1.0). Three principal corpus-linguistic functions were employed: word frequency, collocation, and n-gram analysis. These procedures were applied sequentially and comparatively to uncover lexical frequencies, collocational tendencies, and discursive frames that illuminate the anthropocentric or ecocentric orientations shaping Bangladeshi climate reporting.
4.3.1 Frequency analysis
The first stage involved generating a word frequency list to identify the 50 most frequent content words (nouns, verbs, adjectives, and adverbs). Function words were filtered out to focus on semantically loaded lexical items. The minimum frequency threshold was set at 100 occurrences to ensure that the words analyzed were representative rather than incidental. Such thresholding is consistent with corpus-based convention that frequency cut-offs should balance representativeness with interpretability, as seen in prior studies that adopt thresholds around comparable scales depending on corpus size and research goals (Biber and Barbieri 2007; Cui and Kim 2023).To capture related lexical forms that did not appear in the initial top 50 list, the lemmas of these frequent words were also used to retrieve morphologically related variants (e.g. using economic* to include economically). This ensured a more comprehensive representation of semantically connected terms within the corpus. Frequency counts allow an overall idea of thematic salience of what topics dominate climate coverage and serve as entry points for framing interpretation.
4.3.2 Collocation analysis
The second stage examined collocational patterns surrounding key lexical items central to climate discourse, including climate, environment, carbon, disaster, and policy. Collocation refers to the statistically meaningful co-occurrence of lexical items within a defined span, which can reveal ideologically salient semantic associations and discursive framing tendencies. A collocational window of five words to the left and right (5L–5R) was applied, following established practice in corpus-linguistic research (Gablasova et al. 2017). To ensure reliability and interpretability of results, only collocates occurring at least 20 times in the corpus were retained, consistent with the approach recommended by Baker (2016) to focus on reasonably high-frequency patterns while avoiding spurious low-frequency co-occurrences.
Collocational strength was evaluated using the Log-Likelihood (LL) statistic, which estimates whether a co-occurrence is significantly more frequent than would be expected by chance (Dunning 1993; Gries 2013). LL was selected as the primary association measure given its robustness with medium-to-large corpora and topical convergence (Gablasova et al. 2017). Values above 6.63 (p < 0.01) were treated as statistically significant.
In line with Corpus-Assisted Discourse Studies (CADS) methodology, which combines quantitative corpus outputs and qualitative interpretation (Ancarno 2020), the quantitative collocate list was subsequently subjected to qualitative interpretive filtering to assess thematic relevance to climate-related frames including finance, governance, adaptation, and vulnerability. This combined quantitative–qualitative procedure supports the identification of lexicogrammatical patterns through which agency, responsibility, and environmental relations are constructed.
4.3.3 N-gram analysis
Four-word clusters (4-grams) were extracted from the corpus with a minimum frequency threshold of 20 occurrences to identify recurring multiword patterns in Bangladeshi newspaper coverage of climate change. These clusters capture extended lexical sequences beyond simple word co-occurrence, allowing for systematic examination of phraseological patterns. Following standard practice in corpus-based bundle studies, a threshold of 20 was adopted to balance the inclusion of meaningful recurring patterns with the exclusion of low-frequency, potentially idiosyncratic sequences (Liu et al. 2023). Frame identification was conducted inductively by examining the co-occurrence and thematic proximity of these clusters. Concordance lines and surrounding co-texts were iteratively reviewed to ensure that recurring patterns were captured consistently. These patterns were then prepared for further analysis to examine how linguistic choices may reflect relationships between humans and the environment, particularly regarding anthropocentric and ecocentric perspectives.
5 Findings and discussion
5.1 Lexical frequency and discursive framing of climate change
To identify salient lexical indicators of dominant frames, the fifty most frequent content words (minimum frequency = 100; see Section 4.3.1) were extracted and analyzed for thematic relevance (Table 2). The column “Word (merged form)” consolidates related morphological forms under a single lexical entry, with specific included variants provided in the “Included Forms” column.
50 most frequent words.
| Serial | Word (merged form) | Frequency | Included forms |
|---|---|---|---|
| 1 | climate | 6,342 | climate, climatic, climatologists, climates, climatically, climatologist, climatology |
| 2 | change | 3,444 | – |
| 3 | Bangladesh | 2,075 | – |
| 4 | country | 1,751 | country, countries, countryside, countrywide |
| 5 | global | 1,191 | global, globally, globalisation, globalization |
| 6 | finance | 997 | financial, financing, finance, finances, financed, financially, financiers, financer, financers |
| 7 | world | 985 | world, worldwide, worlds, worldview |
| 8 | nation | 923 | nations, nation, national, nationally, nationwide, nationality, nationism, nationalist |
| 9 | develop | 919 | development, developments, developmental, develop, develops, developed |
| 10 | people | 891 | people, peoples |
| 11 | impact | 847 | impact, impacts, impacted, impacting, impactful |
| 12 | need | 791 | need, needs, needed |
| 13 | adaptation | 753 | adaptation, adaptations |
| 14 | environment | 690 | environmental, environment, environmentally, environmentalists, environments, environmentalism |
| 15 | action | 690 | action, actions, actionable, actionaid |
| 16 | government | 649 | governments, government, governmental |
| 17 | water | 642 | water, waters, waterlogging |
| 18 | risk | 635 | risk, risks, risking, risky |
| 19 | level | 580 | level, levels |
| 20 | emission | 573 | emission, emissions |
| 21 | temperature | 565 | temperature, temperatures |
| 22 | heat | 564 | heat, heatwave, heatwaves, heating, heats, heatstroke, heated |
| 23 | international | 532 | international, internationally, internationalists, internationalization |
| 24 | project | 530 | project, projects |
| 25 | health | 487 | health, healthcare, healthy, healthier |
| 26 | human | 467 | human, humans, humanity, humankind, humanitarian |
| 27 | million | 466 | million, millions |
| 28 | loss | 442 | loss, losses |
| 29 | area | 435 | area, areas |
| 30 | vulnerable | 425 | – |
| 31 | billion | 424 | billion, billions |
| 32 | research | 421 | research, researchers, researcher, researches researching, researched |
| 33 | warming | 395 | warming, warming |
| 34 | carbon | 381 | – |
| 35 | economic | 377 | economic, economics, economically |
| 36 | women | 366 | – |
| 37 | energy | 365 | – |
| 38 | extreme | 354 | extreme, extremes, extremely |
| 39 | support | 352 | support, supported, supports, supporting, supportive |
| 40 | damage | 343 | damage, damages, damaged |
| 41 | time | 335 | – |
| 42 | cop | 331 | – |
| 43 | weather | 328 | – |
| 44 | sea | 328 | sea, seabed, seawater, seafood, seabird |
| 45 | green | 323 | green, greener, greenery, greenland |
| 46 | sustainable | 321 | sustainable, sustainably |
| 47 | rise | 317 | rise, rises, risen |
| 48 | future | 316 | future, futures |
| 49 | food | 295 | food, foods, foodgrain |
| 50 | coastal | 259 | – |
-
Note: The top 50 words appear to lean towards an anthropocentric orientation, where lemmas like: governance, policy, finance, development, people, etc., occur far more frequently than ecocentric terms. Terms such as biodiversity (83), species (75), animal (51), ecology (22), and restoration (12) do not reach the set frequency threshold, which could imply that nature-related vocabulary enjoys comparatively less importance in the discourse. While these results should be viewed cautiously, they are broadly consistent with prior corpus-based studies showing that frequency thresholds tend to highlight high-frequency patterns (Liu et al. 2023). The disparity could also be read in light of Stibbe’s (2021) “stories we live by” concept, where the ‘stories’ of human priorities shape the narratives.
5.1.1 Key framing patterns in climate change coverage
To extend the lexical findings, this section analyzes how the most frequent content words function within their immediate textual environments. Using concordance lines extracted from the top-ranked items in Table 2, dominant thematic frames such as security, food and agriculture, gender, migration, and economic loss were identified. This concordance-based reading uncovers how the lexical items participate in constructing distinct discursive frames. Collectively, these patterns represent the key interpretive frames through which climate change is articulated in Bangladeshi media.
| Just 1-degree centigrade increase of global temperature and further sea level rise will result in inundation of a large area of Bangladesh and thus displacement of 40 million people by the end of this century, the ambassador said. (The Daily Star, 27 January 2019) |
| […] in coastal regions, around 3.6 crore people are now at heightened risk due to rising sea levels, river erosion, severe cyclones, and saltwater intrusion. (The Daily Star, 12 November 2024) |
These narratives in Examples (1) and (2) present climate change as a direct threat to national security and stability, intensifying its discursive weight. Stibbe (2021) notes that security framing often elevates climate discourse’s prominence.
| Bangladesh, as one of the most climate-vulnerable countries globally, faces climate impacts such as floods, cyclones, sea-level rise and salinity intrusion that frequently affect its agriculture, water resources and coastal areas. (The Daily Star, 11 November 2024). |
In Example (3), climate is clearly tied to food production and security, water resources – not just environment but subsistence. This framing makes climate intimately relevant to rural livelihoods and state stability.
| In regions with acute water crisis, it is the women who have to walk miles to collect a pitcher of water, and this extra load gnaws away at whatever time or energy they have left after their already gruelling work hours. (The Daily Star, 18 April 2024) |
The lens in Example (4) shows how women have to suffer more due to climate change. This aligns with global feminist climate discourse, which consistently demonstrates that women experience heightened exposure and reduced adaptive capacity due to socio-economic inequalities and gendered labor divisions (Arora-Jonsson 2011; Dankelman 2010; MacGregor 2010).
| Climate change has influenced […] permanent migration causing economic losses […] and vulnerability.” (Dhaka Tribune, 4 August 2022) |
| Tackling climate change and its consequences will take an astonishing amount of investment – far more than the world has budgeted so far. (BD News, 28 November 2023) |
Examples (5) and (6) reveal that climate change is linguistically framed through an economic-loss narrative, where impacts are quantified in terms of financial depletion, productivity decline, and investment needs rather than ecological degradation or ethical accountability. Consequently, the “solution” to climate change is portrayed through a financial lens, positioning mitigation and adaptation as budgetary challenges instead of ecological imperatives.
This reflects what Olausson (2014) terms the economic rationalization of climate risk, where media prioritize cost, funding, and compensation as the dominant grammar of climate action. Similarly, Boykoff and Boykoff (2007) show that news discourse often translates environmental processes into policy-finance problems to engage institutional actors. Carvalho (2008) further notes that such economic framing reinforces administrative rationalism. Collectively, these tendencies reveal that Bangladeshi media mirror a global pattern where economic security overrides ecological security, sustaining what Stibbe (2021) calls an anthropocentric “story we live by”.
| […] the people of the Sundarbans region, over the last 15 years, have been hit by cyclones once every 17 months, and been victims of floods, river erosion or erratic rainfall almost every year. (The Daily Star, 11 December 2023) |
| Rising waters washed away homes, livelihoods, farmlands, roads, and critical infrastructure, leaving around 7.2 million people in the Sylhet region severely impacted. (The Daily Star, September 2022) |
In Examples (7) and (8), “erratic rainfall” and “rising waters washed away homes” linguistically position natural forces as grammatical agents where rainfall and water perform destructive actions while humans are depicted as passive victims. This grammatical construction effectively naturalizes the crisis, attributing agency and blame to nature rather than to socio-political or industrial causes.
Such agentive representations of nature are common in media discourse. Carvalho (2008) argues that this framing depoliticizes climate responsibility by diverting attention from systemic human drivers to natural phenomena. Stibbe (2021) also observes that these “destructive stories” grammatically erase human accountability, turning ecosystems into threatening agents rather than living systems in distress. In the Bangladeshi context, such language reproduces a “victimhood narrative” (Alexander and Stibbe 2014), where people appear as helpless targets of an uncontrollable natural enemy. This narrative not only obscures anthropogenic responsibility but also hinders ecocentric understanding by linguistically constructing nature as the source rather than the sufferer of crisis.
5.2 Collocation analysis
Collocational patterns were analyzed following the procedure outlined in Section 4.3.2. While both Log-Likelihood (LL) and Mutual Information (MI) are standard association measures (Gries 2013), LL was prioritized because MI tends to overemphasize rare co-occurrences while downplaying frequent, formulaic patterns (Brezina et al. 2015). Consistent with CADS methodology, collocates were selected not only based on LL significance (values > 6.63, p < 0.01; Dunning 1993) but also for their thematic relevance to established environmental frames (finance, governance, adaptation, vulnerability).
In the collocation tables (see Table 3), the asterisk (*) denotes a word stem or wildcard used in AntConc to capture morphological variants. For instance, climat* retrieves climate, climatic, and climatology, while financ* includes finance, financial, and financing. This approach enables the analysis to generalize across morphological variations that convey similar conceptual meanings within a given frame. An Appendix A has been included to provide an expanded list of keywords along with their full sets of collocates, offering a more comprehensive view of the associative patterns that structure climate-related discourse in the corpus.
Frequent collocates.
| Serial | Keyword | Collocate | Frequency LR (total) | Frequency L | Frequency R | Log-likelihood | Mutual information (effect) |
|---|---|---|---|---|---|---|---|
| 1 | Climat* | impacts | 361 | 196 | 165 | 443.408 | 1.914 |
| finance | 364 | 61 | 303 | 416.002 | 1.834 | ||
| financing | 75 | 27 | 48 | 37.887 | 1.145 | ||
| action | 263 | 58 | 205 | 233.032 | 1.578 | ||
| justice | 117 | 19 | 98 | 180.725 | 2.202 | ||
| fund | 180 | 48 | 132 | 139.752 | 1.462 | ||
| funds | 109 | 37 | 72 | 76.708 | 1.382 | ||
| budget | 69 | 23 | 46 | 67.666 | 1.677 | ||
| adaptation | 302 | 80 | 222 | 135.723 | 1.071 | ||
| adapt | 57 | 52 | 5 | 31.161 | 1.197 | ||
| 2 | Governmen* | organizations | 41 | 3 | 38 | 177.513 | 4.467 |
| local | 54 | 51 | 3 | 158.602 | 3.408 | ||
| agencies | 23 | 1 | 22 | 91.574 | 4.211 | ||
| development | 26 | 9 | 17 | 12.529 | 1.129 | ||
| action | 21 | 10 | 11 | 11.611 | 1.221 | ||
| 3 | Financ* | adaptation | 87 | 52 | 35 | 106.739 | 1.945 |
| international | 50 | 43 | 7 | 48.568 | 1.692 | ||
| development | 46 | 27 | 19 | 29.71 | 1.333 | ||
| developing | 25 | 4 | 21 | 20.436 | 1.529 | ||
| support | 45 | 4 | 41 | 82.424 | 2.498 | ||
| green | 41 | 21 | 20 | 60.168 | 2.174 | ||
| sustainable | 26 | 21 | 5 | 20.073 | 1.479 | ||
| 4 | Flood* | cyclones | 66 | 28 | 38 | 331.902 | 4.982 |
| droughts | 58 | 29 | 29 | 109.198 | 3.956 | ||
| extreme | 39 | 28 | 11 | 96.059 | 3.032 | ||
| heat | 33 | 23 | 10 | 60.129 | 2.493 | ||
| storms | 33 | 16 | 17 | 161.953 | 4.898 | ||
| 5 | Adapt* | mitigation | 153 | 66 | 87 | 651.561 | 4.394 |
| plan | 57 | 12 | 45 | 129.283 | 2.866 | ||
| actions | 30 | 7 | 23 | 51.746 | 2.406 | ||
| strategies | 24 | 5 | 19 | 61.938 | 3.122 | ||
| fund | 32 | 10 | 22 | 29.956 | 1.656 | ||
| 6 | Health | public | 47 | 41 | 6 | 174.577 | 4.011 |
| mental | 34 | 32 | 2 | 206.706 | 5.74 | ||
| people | 27 | 14 | 13 | 11.679 | 1.062 | ||
| human | 26 | 23 | 3 | 40.502 | 2.259 | ||
| risks | 26 | 7 | 19 | 60.282 | 2.915 | ||
| 7 | Water | food | 38 | 26 | 12 | 97.934 | 2.323 |
| climate | 36 | 19 | 17 | 93.271 | −1.824 | ||
| drinking | 28 | 25 | 3 | 170.506 | 5.727 | ||
| scarcity | 27 | 6 | 21 | 154.409 | 5.469 | ||
| sea | 20 | 8 | 12 | 17.939 | 1.616 | ||
| 8 | Energy | renewable | 112 | 98 | 14 | 783.613 | 6.383 |
| clean | 32 | 29 | 3 | 174.57 | 5.305 | ||
| climate | 30 | 16 | 14 | 29.088 | −1.213 | ||
| water | 21 | 7 | 14 | 20.157 | 1.682 | ||
| solar | 20 | 9 | 11 | 89.981 | 4.607 | ||
| 9 | Emission | greenhouse | 138 | 124 | 14 | 745.425 | 5.238 |
| carbon | 100 | 92 | 8 | 359.738 | 3.914 | ||
| global | 70 | 50 | 20 | 79.257 | 1.853 | ||
| reduce | 66 | 66 | 0 | 278.741 | 4.389 | ||
| zero | 27 | 20 | 7 | 119.918 | 4.555 | ||
| 10 | People | climate | 99 | 50 | 49 | 35.375 | −0.778 |
| young | 69 | 66 | 3 | 297.821 | 4.445 | ||
| vulnerable | 37 | 31 | 6 | 35.679 | 1.685 | ||
| displaced | 36 | 20 | 16 | 158.897 | 4.519 | ||
| affected | 23 | 11 | 12 | 32.642 | 2.131 | ||
| coastal | 21 | 5 | 16 | 18.195 | 1.583 |
5.2.1 From ecology to economy: the financialization of climate discourse
The collocational pattern of Climat* and Financ* clearly restates that Bangladeshi climate discourse is linguistically dominated by an economic frame. The Climat* node most strongly collocates with impacts (LL = 443.41), finance (LL = 416.00), action (LL = 233.03), justice (LL = 180.73), fund (LL = 139.75), budget (LL = 67.67), and adaptation (LL = 135.72). Similarly, Financ* frequently co-occurs with adaptation (LL = 106.74), support (LL = 82.42), green (LL = 60.17), and sustainable (LL = 20.07). These statistically strong collocations prove that climate discourse is portrayed through monetary and administrative frames, where the problem and its solution are both represented through financial discourse rather than ecological renewal.
| Promised climate finance is still scant, geared towards mitigation, and comes in the form of loans and non-concessional instruments. (Dhaka Tribune, 11 June 2023) |
Example (9) linguistically constructs finance as the missing agent of progress, reinforcing what Stibbe (2021) terms the “more-is-better” story we live by – the belief that growth and funding inherently represent improvement. Such discourse naturalizes expansionary economics as the route to climate recovery, even though this same logic contributes to ecological degradation (see Example 10).
| Climate finance refers to local, national or international financing drawn from public, private and alternative sources of financing to support mitigation and adaptation actions for combating climate change. (Dhaka Tribune, 1 July 2024) |
This again strengthens the tie of solution with economy.
| Bangladesh suffered economic losses worth $3.72 billion and witnessed 185 extreme weather events due to climate change. These warrant justice for Bangladesh. (Daily Sun, 9 October 2024) |
| Climate finance is also needed for mitigation. The Adaptation Gap Report 2023 estimates that due to growing adaptation finance needs and limited flows, globally the current finance gap is around US$194-366 billion per year for adaptation only. (The Financial Express, 25 November 2024) |
In Examples (11) and (12), justice and adaptation have been commodified by reframing moral and ecological objectives in economic terms. This aligns with Boykoff and Boykoff’s (2007) finding that media narratives frequently equate environmental responsibility with financial compensation, and with Carvalho’s (2008) argument that policy discourse tends to administratively rationalize ecological crises. Here, justice is achievable through money, not through structural or ecological transformation.
A related subcluster emerges around Financ* and green (LL = 60.17); and Governmen* and development (LL = 12.53).
| Progress in Bangladesh is hindered by limited capacity at the central bank […] calling for greater development of green financial instruments. (The Financial Express, 18 July 2023) |
| Bangladesh urgently needs to integrate climate change issues within the development process. (Daily Sun, 30 October 2022) |
Examples (13) and (14) reflect a national-interest orientation, where climate solutions are incorporated within the framework of economic modernization. The appeal to “green bonds” and “integration within development” foregrounds financial innovation over ecosystem repair. Nerlich et al. (2010) describe this as the “green growth” narrative, in which sustainability is domesticated within neoliberal logic.
5.2.2 Constructing human vulnerability through climate change narratives
Beyond the financial and disaster framings, the collocational results reveal that Bangladeshi climate discourse frequently ties climate change to essential life domains such as education, health, and social vulnerability. Strong associations such as education with children (LL = 87.49), health with public (LL = 174.58), health with mental (LL = 206.71), and women with girls (LL = 135.80) highlight that climate change is linguistically framed through its effects on human welfare rather than on ecological systems. Similar human-interest framings have been noted across global media, where climate coverage increasingly emphasizes human impacts to enhance emotional engagement and moral salience (O’Neill et al. 2015; Schäfer and O’Neill 2017). In doing so, it can mobilize empathy, public concern, or calls for action in ways that pure economic framings might not.
This pattern is exemplified in the Dhaka Tribune’s report:
| Record-breaking heat last month that prompted governments in Asia to close schools offers fresh evidence of how climate change is threatening the education of millions of children. (Dhaka Tribune, 10 May 2024) |
In Example (15), the education and children collocation transforms a climatic event into a social crisis, showing how a fundamental right like education is jeopardized by environmental change. Such framing aligns with Boykoff (2011), who observes that journalists often domesticate climate narratives by linking them to everyday life domains.
A similar strategy appears in gendered framings. For example:
| Women and girls are always backbenchers in case of any emergency. Women and children are 14 times more vulnerable during disasters in developing countries. (Dhaka Tribune, 6 February 2023) |
Example (16) positions women and children as emblematic victims, echoing findings from Arora-Jonsson (2011) and Dankelman (2010), who argue that media and policy discourses often construct gendered vulnerability to evoke empathy and justify intervention. The strong co-occurrence of women with children thus reproduces what Pepermans and Maeseele (2016) call “humanization framing,” where social categories stand in metonymically for climate impacts on society at large.
Overall, these collocational trends demonstrate that Bangladeshi newspapers not only frame climate change as an economic and environmental issue but also as a threat to everyday life such as education, health, and gendered well-being. These positive emotional framings support O’Neill et al.’s (2013) view that human-centered narratives can enhance perceived relevance and motivate engagement with climate action.
5.2.3 Disaster narratives and moral displacement in climate reporting
The collocational profile of Flood with cyclones (LL = 331.90) and droughts (LL = 109.19) reveals a dominant frame of disaster victimhood, where natural phenomena are foregrounded as causal agents of human suffering.
| Droughts, floods and other extreme weather events are also expected to hit global crops, leading to rising hunger […]. (Dhaka Tribune, 10 November 2024) |
| Droughts, floods and river erosion across the region have left millions of children homeless, hungry, lacking healthcare and safe water – and in many cases out of school, UNICEF officials said. (Bangladesh News24, 9 May 2022) |
In Examples (17) and (18), nature is seen as the agent of destruction and human loss. Through such material process constructions, climatic elements (droughts, floods, erosion) perform destructive actions, while human subjects are grammatically positioned as patients or victims.
Similarly, human displacement is frequently attributed to climatic forces, which are linguistically foregrounded as the causal agents of social disruption. For instance:
| The country is already seeing the effects of climate change on migration, with deadly and destructive hurricanes driving migrants to leave their homes in Central America and flee to the United States through Mexico. (Bangladesh News 24, 22 October 2021) |
In Example (19), hurricanes, as natural phenomena, are explicitly described as “deadly” and “destructive,” which positions them as the main drivers of migration. By attributing agency to hurricanes and other natural events, the press effectively portrays these occurrences as causal forces. This approach aligns with Hulme’s (2008) analysis, which identifies the prevalent framing of climate change as a catastrophic event. Such a discourse emphasizes the immediate and severe impacts on human societies, thereby reinforcing an anthropocentric perspective where ecological events are considered significant primarily for their consequences on human populations, rather than their intrinsic ecological value.
However, a subtle change in agency occurs in Example (20):
| Many residents of Bangladesh, Mozambique, and Pakistan do not even own cars, yet they are suffering from the floods, cyclones, and rising sea levels that have resulted from developed-country emissions. (Dhaka Tribune, 29 December 2022). |
Here, human agency emerges, but in a distal and abstract form of “developed-country emissions” which deflects culpability from local to global actors. Such distributed accountability, while ethically charged, remains grammatically passive, framing humans as sufferers rather than participants in ecological processes. This linguistic pattern reflects a moral displacement within Bangladeshi climate discourse: nature is the proximate villain, and humanity, though implicated, remains rhetorically insulated from direct blame (Stibbe 2021).
5.2.4 From crisis to care: constructing ecological responsibility in climate narratives
Beyond crisis narratives, solution-oriented framings are also evident in the corpus, supported by prominent collocational patterns. The Energy node shows high LL values with renewable (783.61), clean (174.57), efficiency (135.92), transition (82.84), and solar (89.98), reflecting a discourse of technological optimism and sustainable transition. Likewise, Emission collocates with greenhouse (745.43), reduce (278.74), zero (119.92), and cut (99.14), signifying an emphasis on mitigation and responsibility.
| “Greenhouse gas emissions from factories have brought us dangerously close to irreversible changes in the environment, hinting at more climate-related disasters like tidal floods, wildfires, droughts, hurricanes, heat waves, extreme rainfall, etc” (Bangladesh News 24, 24 October 2022) |
As the Example (21) illustrates, agentive nominalization (e.g. factories, emissions) linguistically attributes accountability to human activity rather than abstract natural processes. This pattern is consistent with ecolinguistic analyses of environmental responsibility (Halliday 2001 [1990]).
| Unless countries urgently reduce their planet-heating emissions, extreme weather “will only become more intense”, she said. Greenhouse gas emissions from burning fossil fuels are the main cause of climate change. (Bangladesh News 24, 6 September 2024) |
Similarly, Example (22) foregrounds human agency in mitigation, aligning with the reconstructive discourse Stibbe (2021) advocates, where language promotes ecological care rather than destruction.
These positive framings echo findings by Fløttum et al. (2016) and Nerlich and Koteyko (2009), who note that climate journalism increasingly blends risk and responsibility frames, presenting energy transition as both a necessity and an opportunity for sustainable reform.
5.3 Four-grams analyses
The subsequent analysis focused on refining and interpreting the four-word clusters to identify meaningful phraseological patterns within the corpus. Variants such as effect of climate change and effects of climate change were merged to ensure interpretive clarity, while purely functional clusters (e.g. one of the major) were excluded to foreground semantically meaningful patterns (Sinclair 2004). Multi-word institutional expressions such as United Nations Framework Convention on Climate Change (UNFCCC) and Intergovernmental Panel on Climate Change (IPCC) were retained as cohesive lexical units, even when they exceeded four-gram limits. This was necessary because these entities appeared in multiple overlapping clusters (e.g. united nations framework convention, framework convention on climate, convention on climate change, intergovernmental panel on climate, and on climate change IPCC) and thus represented single referential constructs rather than separate phrases. The raw, unmerged cluster list is provided in Appendix B to maintain transparency and replicability.
The clusters in Table 4 were selected based on their thematic salience. Salience here refers to their relevance to identifiable environmental frames such as crisis, policy, finance, or solutions – rather than on frequency alone. This thematically informed filtering follows Baker’s (2006) recommendation that corpus findings should be interpreted in light of broader discursive contexts, which ensures that recurrent forms are analytically tied to communicative functions. Thematically guided selection is particularly crucial in ecolinguistic research, where the focus lies not merely on word frequency but on how recurrent phrasal patterns construct ecological or anthropocentric worldviews (Stibbe 2021). It is important to acknowledge that the study did not incorporate dispersion measures, which could provide additional insight into the distribution of multi-word patterns across texts.
20 frequent 4-grams.
| Serial | Four-grams | Frequency |
|---|---|---|
| 1 | Impact(s) of climate change | 366 |
| 2 | Effect(s) of climate change | 228 |
| 3 | United nations framework convention on CC (climate change) | 250 |
| 4 | Intergovernmental panel on CC (climate change) | 103 |
| 5 | due to climate change | 103 |
| 6 | Bangladesh govt and national strategies | 97 |
| 7 | environment, forest and climate | 76 |
| 8 | To tackle climate change | 68 |
| 9 | to address climate change | 49 |
| 10 | change strategy and action | 48 |
| 11 | Loss and damage (fund) | 41 |
| 12 | climate change trust fund | 36 |
| 13 | to combat climate change | 33 |
| 14 | caused by climate change | 31 |
| 15 | human induced climate change | 26 |
| 16 | the Bay of Bengal | 26 |
| 17 | Climate change in Bangladesh | 25 |
| 18 | of the Paris agreement | 21 |
| 19 | Adapt to climate change | 21 |
| 20 | vulnerable to climate change | 20 |
5.3.1 Framing climate change: from financial solutions to moral struggle
The 4-gram cluster profile reinforces the economic preoccupation already evident in the lexical and collocational analyses. Financially charged clusters such as loss and damage fund (41) and climate change trust fund (36) highlight that the imagined “solution” to climate change is constructed through fiscal mechanisms rather than ecological repair.
| The country will be vocal in making the loss and damage fund operational with simplified access and regular replenishment cycles. (The Daily Star, 11 November 2024) |
Example (23) situates environmental recovery within the lens of budgetary circulation and national gain. This pattern corresponds with what Boykoff and Boykoff (2007) call the economization of climate discourse, where environmental action is linguistically tied to finance, policy, and compensation. It also resonates with Stibbe’s (2021) critique of the “more-is-better” narrative, which conflates financial expansion with ecological progress.
Simultaneously, the high frequencies of impact(s) of climate change (366) and effect(s) of climate change (228) index a crisis frame grounded in vulnerability.
| The impacts of climate change are a matter of concern for Bangladesh, where lives and livelihoods depend mainly on agriculture, is exposed to a great danger. (Daily Sun, 10 February 2018) |
Example (24) reveals how existential threats link climate to food security and human survival.
| We all know women are more vulnerable to climate change. And UN Women is dedicated to ensuring gender equality and the empowerment of women,” said the organisation’s programme specialist, Dilruba Haider. (Dhaka Tribune, 27 October 2022) |
Parallelly, Example (25) brings climate change under gendered precarity discourse, aligning with global feminist climate narratives that foreground disproportionate female suffering (Dankelman 2010). Collectively, these clusters depict climate change as an ongoing humanitarian emergency rather than a planetary imbalance.
Another significant frame evident in the corpus is the war or battle metaphor, where climate action is conceptualized as a collective struggle demanding courage, leadership, and moral commitment. This metaphor is linguistically realized through recurrent lexical clusters such as to tackle climate change (41), to combat climate change (33), and the fight against climate change (27). Examples (26)–(28) illustrate this frame clearly:
| It is also important that local women and men are included in designing programmes to tackle climate change and address its impacts, as well as make sure that female-headed households are given the extra support they need. (Financial Express, 29 January 2020) |
| On April 9, the European Court of Human Rights (ECHR) ruled that the Swiss government had violated the human rights of its citizens by failing to do enough to combat climate change. (Bangladesh News 24, 3 May 2024) |
| Bangladesh has acted as a sort of leader in the fight against climate change, which is why we need to double down on this role while making sure we get what we are owed due to the actions of first-world nations. (Dhaka Tribune, 2 March 2024) |
These examples collectively frame climate action as a battlefront, where human agency is valorized through verbs such as tackle, combat, and fight. The metaphor unites diverse actors like governments, communities, and individuals under a shared sense of moral and strategic commitment. It constructs climate response not merely as policy compliance but as a collective duty, thereby promoting solidarity and national resolve. The Dhaka Tribune excerpt, in particular, extends this metaphor by positioning Bangladesh as a “leader” in this global struggle, reinforcing a sense of national responsibility and pride.
As Nerlich and Koteyko (2009) observe, such militarized rhetoric can be persuasive in mobilizing collective action by evoking urgency and moral purpose. However, this framing also risks simplifying complex ecological dynamics into anthropocentric struggles, emphasizing human control and heroism over ecological interdependence.
A smaller but ideologically significant cluster is human-induced climate change, which highlights anthropogenic accountability.
| Advocates of the system believe it could save lives amid fears that human-induced climate change makes heatwaves more frequent, more intense and longer lasting. (Daily Sun, 29 July 2022) |
| There are three ways in which human induced climate change affects us and hence three ways to tackle the problem. (Dhaka Tribune, 17 March 2018) |
| Climate change poses a fundamental threat to places, species and people’s livelihoods. The impacts of human-induced climate change have already become a reality and it is occurring on a global scale. It is disrupting national economies and affecting lives. Increased heat waves, droughts, and floods stemming from climate change are exceeding plants and animals’ tolerance thresholds, driving mass mortalities in species such as trees and corals. (Financial Express, 18 May 2022) |
Examples (29)–(31) collectively contribute to what Stibbe (2021) terms “beneficial discourse” – language that foregrounds human responsibility while fostering awareness of interspecies interdependence. They linguistically position humans as causal agents rather than passive victims, signaling a discursive movement toward self-implication and moral accountability. This tendency, though limited in frequency, resonates with critical-ecological framings (Stibbe 2021), which advocate recognition of human agency as a prerequisite for genuine environmental transformation.
In sum, Bangladeshi newspaper coverage of climate change is dominated by anthropocentric lexical and collocational patterns, with economic, security, disaster, and gender frames foregrounding human concerns. While the discourse largely positions humans as beneficiaries, victims, or moral agents within financial and administrative frameworks, there lies a glimmer of hope in the form of scantly lit beacons of the existing frames, where humans are still at the center but at the center of blame and responsibility that they have to take and fulfill.
6 Conclusions
This study examined how climate change is represented in Bangladeshi newspapers through lexical, collocational, and multi-word cluster analyses. Across all three levels, economic, human welfare, and policy concerns were foregrounded, reflecting an anthropocentric orientation. Climate change was thus framed not only as an environmental crisis but also through economic, social, educational, and gendered lenses, which heightened its perceived urgency. Ecocentric and critically ecological frames were present but limited in number, indicating that coverage of environmental responsibility and interspecies interdependence remains underdeveloped and warrants further amplification. Overall, the findings reveal a media discourse that situates humans as central agents, beneficiaries, or victims while signaling the potential for more robust ecologically informed reporting (Boykoff and Boykoff 2007; Carvalho 2008; Stibbe 2021). The study is confined to print media and does not consider digital or social media platforms, which may exhibit alternative discursive patterns.
While this study remains focused on environmental language, particularly climate change reporting, it approaches the discourse through multiple lenses – economic, social, and ethical. By tracing how human and ecological concerns intertwine within these narratives, the analysis highlights that even explicitly environmental communication can reveal broader patterns of human-nature interdependence and moral positioning in the public sphere.
The findings underscore the value of corpus-assisted ecolinguistics for tracing the discursive construction of climate change. While human-centered narratives dominate, the emergent attention to anthropogenic responsibility offers a basis for fostering more ethically and ecologically informed communication. Future research could extend this approach to diverse media genres, regional languages, and longitudinal analyses to monitor shifts in ecological awareness and inform strategies for sustainable environmental engagement.
Appendix A: Expanded collocation list
| Serial | Keyword | Collocate | Frequency LR (Total) | Frequency L | Frequency R | Log-likelihood (LL) | Mutual Information (Effect) |
|---|---|---|---|---|---|---|---|
| 1. | Climat* | change | 3559 | 194 | 3365 | 6557.669 | 2.423 |
| climate | 556 | 278 | 278 | 455.227 | −1.12 | ||
| impacts | 361 | 196 | 165 | 443.408 | 1.914 | ||
| finance | 364 | 61 | 303 | 416.002 | 1.834 | ||
| action | 263 | 58 | 205 | 233.032 | 1.578 | ||
| induced | 116 | 36 | 80 | 192.814 | 2.303 | ||
| effects | 168 | 137 | 31 | 187.247 | 1.808 | ||
| justice | 117 | 19 | 98 | 180.725 | 2.202 | ||
| related | 163 | 24 | 139 | 171.482 | 1.747 | ||
| crisis | 145 | 12 | 133 | 165.581 | 1.834 | ||
| vulnerable | 213 | 95 | 118 | 149.421 | 1.379 | ||
| fund | 180 | 48 | 132 | 139.752 | 1.462 | ||
| adaptation | 302 | 80 | 222 | 135.723 | 1.071 | ||
| resilience | 140 | 38 | 102 | 124.14 | 1.58 | ||
| adverse | 87 | 80 | 7 | 107.525 | 1.924 | ||
| resilient | 103 | 18 | 85 | 103.891 | 1.705 | ||
| fight | 68 | 63 | 5 | 98.944 | 2.123 | ||
| impact | 140 | 91 | 49 | 92.143 | 1.33 | ||
| against | 97 | 91 | 6 | 88.708 | 1.609 | ||
| framework | 78 | 59 | 19 | 86.483 | 1.803 | ||
| tackle | 68 | 65 | 3 | 82.923 | 1.908 | ||
| tackling | 53 | 52 | 1 | 81.533 | 2.197 | ||
| bangladesh | 589 | 262 | 327 | 80.324 | 0.559 | ||
| trust | 60 | 8 | 52 | 80.124 | 2.016 | ||
| panel | 53 | 42 | 11 | 78.675 | 2.149 | ||
| funds | 109 | 37 | 72 | 76.708 | 1.382 | ||
| convention | 57 | 52 | 5 | 76.302 | 2.019 | ||
| intergovernmental | 40 | 35 | 5 | 75.316 | 2.488 | ||
| address | 109 | 96 | 13 | 74.804 | 1.362 | ||
| water | 36 | 16 | 20 | 73.723 | −1.659 | ||
| risk | 157 | 47 | 110 | 72.593 | 1.089 | ||
| plan | 120 | 37 | 83 | 71.286 | 1.255 | ||
| changing | 75 | 64 | 11 | 69.053 | 1.616 | ||
| budget | 69 | 23 | 46 | 67.666 | 1.677 | ||
| risks | 106 | 33 | 73 | 61.619 | 1.239 | ||
| addressing | 68 | 56 | 12 | 60.284 | 1.58 | ||
| vulnerability | 64 | 25 | 39 | 60.069 | 1.634 | ||
| challenges | 99 | 51 | 48 | 54.17 | 1.198 | ||
| combat | 44 | 43 | 1 | 53.451 | 1.904 | ||
| mitigation | 102 | 23 | 79 | 49.809 | 1.123 | ||
| issues | 94 | 31 | 63 | 47.777 | 1.149 | ||
| smart | 29 | 2 | 27 | 47.382 | 2.279 | ||
| caused | 76 | 57 | 19 | 46.985 | 1.284 | ||
| prosperity | 35 | 4 | 31 | 40.912 | 1.861 | ||
| mujib | 25 | 24 | 1 | 40.414 | 2.265 | ||
| financing | 75 | 27 | 48 | 37.887 | 1.145 | ||
| people | 96 | 49 | 47 | 37.609 | −0.811 | ||
| ipcc | 50 | 14 | 36 | 36.835 | 1.42 | ||
| disasters | 86 | 18 | 68 | 36.193 | 1.034 | ||
| were | 34 | 12 | 22 | 35.008 | −1.244 | ||
| shocks | 28 | 7 | 21 | 34.931 | 1.935 | ||
| environment | 116 | 73 | 43 | 33.305 | 0.837 | ||
| policies | 68 | 19 | 49 | 31.813 | 1.097 | ||
| mitigate | 36 | 34 | 2 | 31.487 | 1.568 | ||
| adapt | 57 | 52 | 5 | 31.161 | 1.197 | ||
| plans | 56 | 13 | 43 | 30.995 | 1.206 | ||
| migrants | 36 | 5 | 31 | 30.709 | 1.545 | ||
| energy | 30 | 14 | 16 | 30.025 | −1.229 | ||
| year | 68 | 38 | 30 | 29.35 | −0.848 | ||
| carbon | 33 | 18 | 15 | 28.521 | −1.154 | ||
| actions | 72 | 27 | 45 | 27.69 | 0.983 | ||
| victims | 28 | 17 | 11 | 26.968 | 1.659 | ||
| last | 24 | 8 | 16 | 26.287 | −1.278 | ||
| refugees | 28 | 2 | 26 | 26.115 | 1.628 | ||
| science | 48 | 21 | 27 | 26.049 | 1.192 | ||
| unfccc | 41 | 7 | 34 | 24.435 | 1.257 | ||
| during | 21 | 8 | 13 | 24.097 | −1.304 | ||
| strategy | 35 | 3 | 32 | 23.78 | 1.355 | ||
| sea | 28 | 8 | 20 | 23.708 | −1.144 | ||
| index | 24 | 2 | 22 | 23.489 | 1.675 | ||
| variability | 20 | 3 | 17 | 21.91 | 1.791 | ||
| global | 285 | 201 | 84 | 21.536 | 0.41 | ||
| temperatures | 26 | 10 | 16 | 21.359 | −1.129 | ||
| policy | 81 | 27 | 54 | 21.231 | 0.797 | ||
| migration | 46 | 11 | 35 | 20.898 | 1.079 | ||
| level | 34 | 17 | 17 | 20.85 | −0.992 | ||
| million | 38 | 17 | 21 | 20.388 | −0.935 | ||
| human | 111 | 74 | 37 | 19.996 | 0.65 | ||
| projects | 99 | 28 | 71 | 19.774 | 0.688 | ||
| relevant | 25 | 5 | 20 | 19.443 | 1.465 | ||
| emissions | 55 | 30 | 25 | 18.77 | −0.762 | ||
| who | 41 | 25 | 16 | 18.586 | −0.867 | ||
| consequences | 41 | 35 | 6 | 18.065 | 1.061 | ||
| 2. | Governmen* | non | 45 | 25 | 20 | 178.088 | 4.19 |
| organisations | 41 | 3 | 38 | 177.513 | 4.467 | ||
| local | 54 | 51 | 3 | 158.602 | 3.408 | ||
| agencies | 23 | 1 | 22 | 91.574 | 4.211 | ||
| ngos | 21 | 1 | 20 | 82.578 | 4.174 | ||
| private | 27 | 4 | 23 | 48.5 | 2.471 | ||
| bangladesh | 83 | 34 | 49 | 33.61 | 1.02 | ||
| national | 28 | 22 | 6 | 33.421 | 1.917 | ||
| sector | 20 | 6 | 14 | 25.525 | 1.998 | ||
| government | 26 | 16 | 10 | 19.646 | 1.461 | ||
| international | 25 | 7 | 18 | 15.666 | 1.311 | ||
| development | 26 | 9 | 17 | 12.529 | 1.129 | ||
| action | 21 | 10 | 11 | 11.611 | 1.221 | ||
| 3. | Finance* | climate | 500 | 375 | 125 | 359.334 | 1.396 |
| adaptation | 87 | 52 | 35 | 106.739 | 1.945 | ||
| change | 69 | 31 | 38 | 13.762 | −0.596 | ||
| private | 59 | 33 | 26 | 141.993 | 2.979 | ||
| finance | 58 | 31 | 27 | 65.618 | 1.853 | ||
| international | 50 | 43 | 7 | 48.568 | 1.692 | ||
| institutions | 50 | 3 | 47 | 166.085 | 3.7 | ||
| development | 46 | 27 | 19 | 29.71 | 1.333 | ||
| support | 45 | 4 | 41 | 82.424 | 2.498 | ||
| sources | 44 | 21 | 23 | 123.173 | 3.295 | ||
| green | 41 | 21 | 20 | 60.168 | 2.174 | ||
| billion | 40 | 26 | 14 | 39.633 | 1.712 | ||
| blended | 39 | 32 | 7 | 222.258 | 5.423 | ||
| sector | 38 | 12 | 26 | 61.228 | 2.304 | ||
| mitigation | 31 | 16 | 15 | 42.154 | 2.074 | ||
| private | 59 | 33 | 26 | 141.993 | 2.979 | ||
| banks | 30 | 19 | 11 | 82.825 | 3.265 | ||
| need | 30 | 20 | 10 | 14.607 | 1.135 | ||
| funds | 29 | 6 | 23 | 41.52 | 2.141 | ||
| public | 27 | 15 | 12 | 39.72 | 2.178 | ||
| fund | 27 | 13 | 14 | 18.852 | 1.395 | ||
| sustainable | 26 | 21 | 5 | 20.073 | 1.479 | ||
| developing | 25 | 4 | 21 | 20.436 | 1.529 | ||
| risk | 25 | 16 | 9 | 11.655 | 1.108 | ||
| resources | 25 | 3 | 22 | 27.994 | 1.843 | ||
| bank | 22 | 12 | 10 | 29.409 | 2.053 | ||
| provide | 22 | 15 | 7 | 33.8 | 2.238 | ||
| access | 22 | 18 | 4 | 33.127 | 2.21 | ||
| projects | 22 | 6 | 16 | 11.59 | 1.187 | ||
| system | 22 | 3 | 19 | 29.602 | 2.061 | ||
| risks | 21 | 7 | 14 | 18.015 | 1.573 | ||
| needs | 21 | 3 | 18 | 18.878 | 1.617 | ||
| financial | 20 | 7 | 13 | 13.195 | 1.35 | ||
| 4. | Flood* | cyclones | 66 | 28 | 38 | 331.902 | 4.982 |
| climate | 61 | 35 | 26 | 16.243 | −0.68 | ||
| droughts | 58 | 29 | 29 | 321.534 | 5.354 | ||
| extreme | 39 | 28 | 11 | 96.059 | 3.032 | ||
| heat | 33 | 23 | 10 | 60.129 | 2.493 | ||
| storms | 33 | 16 | 17 | 161.953 | 4.898 | ||
| drought | 30 | 12 | 18 | 109.198 | 3.956 | ||
| flash | 29 | 24 | 5 | 196.32 | 6.218 | ||
| events | 27 | 16 | 11 | 70.683 | 3.159 | ||
| disasters | 26 | 21 | 5 | 60.95 | 2.936 | ||
| water | 26 | 9 | 17 | 20.541 | 1.499 | ||
| sea | 25 | 10 | 15 | 40.65 | 2.321 | ||
| erosion | 25 | 6 | 19 | 124.331 | 4.947 | ||
| natural | 23 | 17 | 6 | 43.834 | 2.567 | ||
| risk | 23 | 10 | 13 | 28.118 | 1.946 | ||
| coastal | 23 | 13 | 10 | 42.325 | 2.51 | ||
| areas | 21 | 8 | 13 | 29.721 | 2.129 | ||
| weather | 20 | 12 | 8 | 24.899 | 1.968 | ||
| 5. | Adapt* | climate | 393 | 243 | 150 | 175.012 | 1.065 |
| change | 207 | 126 | 81 | 82.048 | 1.005 | ||
| mitigation | 153 | 66 | 87 | 651.561 | 4.394 | ||
| finance | 64 | 22 | 42 | 82.862 | 2.011 | ||
| plan | 57 | 12 | 45 | 129.283 | 2.866 | ||
| national | 56 | 55 | 1 | 90.888 | 2.314 | ||
| projects | 47 | 8 | 39 | 75.435 | 2.298 | ||
| resilience | 40 | 10 | 30 | 71.417 | 2.458 | ||
| efforts | 39 | 11 | 28 | 77.828 | 2.639 | ||
| impacts | 37 | 8 | 29 | 23.29 | 1.313 | ||
| measures | 36 | 6 | 30 | 89.752 | 3.051 | ||
| fund | 32 | 10 | 22 | 29.956 | 1.656 | ||
| capacity | 31 | 9 | 22 | 52.522 | 2.379 | ||
| actions | 30 | 7 | 23 | 51.746 | 2.406 | ||
| billion | 29 | 21 | 8 | 17.074 | 1.264 | ||
| led | 29 | 27 | 2 | 66.792 | 2.897 | ||
| action | 29 | 13 | 16 | 12.991 | 1.083 | ||
| need | 29 | 16 | 13 | 13.401 | 1.102 | ||
| help | 27 | 22 | 5 | 31.87 | 1.903 | ||
| strategies | 24 | 5 | 19 | 61.938 | 3.122 | ||
| locally | 23 | 22 | 1 | 105.159 | 4.63 | ||
| developing | 22 | 13 | 9 | 14.711 | 1.361 | ||
| based | 21 | 16 | 5 | 15.32 | 1.431 | ||
| financing | 21 | 12 | 9 | 26.757 | 1.994 | ||
| local | 20 | 12 | 8 | 13.562 | 1.372 | ||
| sector | 20 | 16 | 4 | 13.95 | 1.394 | ||
| 6. | Education | children | 21 | 10 | 11 | 87.492 | 4.363 |
| 7. | Water | water | 60 | 31 | 29 | 97.934 | 2.323 |
| food | 38 | 26 | 12 | 77.71 | 2.684 | ||
| climate | 36 | 19 | 17 | 93.271 | −1.824 | ||
| drinking | 28 | 25 | 3 | 170.506 | 5.727 | ||
| change | 27 | 18 | 9 | 34.892 | −1.374 | ||
| scarcity | 27 | 6 | 21 | 154.409 | 5.469 | ||
| management | 26 | 3 | 23 | 54.983 | 2.744 | ||
| agriculture | 21 | 14 | 7 | 28.547 | 2.076 | ||
| energy | 21 | 14 | 7 | 16.617 | 1.501 | ||
| land | 21 | 12 | 9 | 29.349 | 2.112 | ||
| resources | 21 | 2 | 19 | 30.603 | 2.167 | ||
| sea | 20 | 8 | 12 | 17.939 | 1.616 | ||
| 8. | Health* | change | 75 | 45 | 30 | 10.026 | 0.558 |
| public | 47 | 41 | 6 | 174.577 | 4.011 | ||
| mental | 34 | 32 | 2 | 206.706 | 5.74 | ||
| world | 31 | 23 | 8 | 16.157 | 1.18 | ||
| people | 27 | 14 | 13 | 11.679 | 1.062 | ||
| risks | 26 | 7 | 19 | 60.282 | 2.915 | ||
| human | 26 | 23 | 3 | 40.502 | 2.259 | ||
| health | 25 | 12 | 13 | 29.776 | 1.915 | ||
| water | 22 | 7 | 15 | 14.195 | 1.333 | ||
| impacts | 22 | 12 | 10 | 19.012 | 1.581 | ||
| issues | 22 | 1 | 21 | 46.818 | 2.757 | ||
| who | 21 | 3 | 18 | 24.06 | 1.871 | ||
| 9. | Women | girls | 22 | 2 | 20 | 135.798 | 5.821 |
| women | 34 | 17 | 17 | 84.978 | 3.062 | ||
| children | 20 | 6 | 14 | 48.527 | 3.005 | ||
| 10. | Children | million | 31 | 24 | 7 | 98.198 | 3.595 |
| women | 20 | 14 | 6 | 48.527 | 3.005 | ||
| 11. | Energy | renewable | 112 | 98 | 14 | 783.613 | 6.383 |
| energy | 38 | 19 | 19 | 102.928 | 3.231 | ||
| clean | 32 | 29 | 3 | 174.57 | 5.305 | ||
| climate | 30 | 16 | 14 | 29.088 | −1.213 | ||
| efficiency | 22 | 2 | 20 | 135.917 | 5.825 | ||
| sources | 22 | 4 | 18 | 73.884 | 3.745 | ||
| water | 21 | 7 | 14 | 20.157 | 1.682 | ||
| transition | 20 | 4 | 16 | 82.837 | 4.341 | ||
| use | 20 | 13 | 7 | 44.786 | 2.851 | ||
| solar | 20 | 9 | 11 | 89.981 | 4.607 | ||
| 12. | Emisson | greenhouse | 138 | 124 | 14 | 745.425 | 5.238 |
| gas | 132 | 127 | 5 | 686.334 | 5.094 | ||
| carbon | 100 | 92 | 8 | 359.738 | 3.914 | ||
| global | 70 | 50 | 20 | 79.257 | 1.853 | ||
| reduce | 66 | 66 | 0 | 278.741 | 4.389 | ||
| climate | 60 | 28 | 32 | 26.967 | −0.863 | ||
| reducing | 28 | 28 | 0 | 107.164 | 4.097 | ||
| zero | 27 | 20 | 7 | 119.918 | 4.555 | ||
| reduction | 26 | 10 | 16 | 97.431 | 4.037 | ||
| net | 25 | 18 | 7 | 108.015 | 4.466 | ||
| cut | 22 | 20 | 2 | 99.142 | 4.603 | ||
| warming | 20 | 13 | 7 | 16.598 | 1.544 | ||
| 13. | People | climate | 99 | 50 | 49 | 35.375 | −0.778 |
| million | 79 | 75 | 4 | 187.561 | 2.952 | ||
| young | 69 | 66 | 3 | 297.821 | 4.445 | ||
| more | 64 | 49 | 15 | 32.393 | 1.159 | ||
| change | 52 | 33 | 19 | 21.986 | −0.842 | ||
| vulnerable | 37 | 31 | 6 | 35.679 | 1.685 | ||
| displaced | 36 | 20 | 16 | 158.897 | 4.519 | ||
| many | 35 | 30 | 5 | 36.387 | 1.763 | ||
| millions | 31 | 31 | 0 | 102.23 | 3.686 | ||
| living | 30 | 2 | 28 | 82.877 | 3.269 | ||
| billion | 24 | 23 | 1 | 11.725 | 1.137 | ||
| affected | 23 | 11 | 12 | 32.642 | 2.131 | ||
| areas | 23 | 8 | 15 | 17.446 | 1.464 | ||
| livelihoods | 22 | 13 | 9 | 54.81 | 3.052 | ||
| coastal | 21 | 5 | 16 | 18.195 | 1.583 | ||
| lives | 20 | 9 | 11 | 40.325 | 2.659 | ||
| 14. | Risk* | climate | 259 | 178 | 81 | 120.793 | 1.094 |
| change | 104 | 55 | 49 | 18.105 | 0.642 | ||
| disaster | 46 | 41 | 5 | 145.494 | 3.585 | ||
| health | 33 | 24 | 9 | 39.782 | 1.928 | ||
| financial | 29 | 13 | 16 | 54.149 | 2.532 | ||
| high | 27 | 25 | 2 | 45.224 | 2.364 | ||
| related | 24 | 18 | 6 | 38.446 | 2.299 | ||
| insurance | 24 | 3 | 21 | 90.331 | 4.047 | ||
| management | 23 | 4 | 19 | 45.741 | 2.637 | ||
| reduction | 23 | 1 | 22 | 76.292 | 3.707 | ||
| impacts | 22 | 5 | 17 | 11.695 | 1.194 | ||
| physical | 22 | 21 | 1 | 99.026 | 4.596 | ||
| index | 21 | 2 | 19 | 100.331 | 4.798 | ||
| 15. | Migration | climate | 45 | 35 | 10 | 19.927 | 1.064 |
| 16. | Mitigate* | adaptation | 150 | 84 | 66 | 619.113 | 4.304 |
| efforts | 29 | 9 | 20 | 98.342 | 3.769 | ||
| actions | 27 | 5 | 22 | 93.017 | 3.811 | ||
| change | 105 | 62 | 43 | 92.125 | 1.583 | ||
| climate | 153 | 81 | 72 | 91.778 | 1.262 | ||
| finance | 22 | 12 | 10 | 28.774 | 2.028 | ||
| impacts | 21 | 3 | 18 | 28.029 | 2.054 |
Appendix B: Raw, unmerged list of 4-grams
| Serial | 4-grams | Frequency | Normalized frequency |
|---|---|---|---|
| 1. | impacts of climate change | 148 | 449.665 |
| 2. | effects of climate change | 113 | 343.325 |
| 3. | due to climate change | 80 | 243.062 |
| 4. | the impacts of climate | 74 | 224.832 |
| 5. | of climate change and | 71 | 215.718 |
| 6. | impact of climate change | 68 | 206.603 |
| 7. | the effects of climate | 68 | 206.603 |
| 8. | of climate change the | 59 | 179.258 |
| 9. | of climate change on | 53 | 161.029 |
| 10. | to address climate change | 49 | 148.876 |
| 11. | convention on climate change | 48 | 145.837 |
| 12. | framework convention on climate | 47 | 142.799 |
| 13. | of climate change in | 43 | 130.646 |
| 14. | to tackle climate change | 41 | 124.569 |
| 15. | the impact of climate | 40 | 121.531 |
| 16. | panel on climate change | 39 | 118.493 |
| 17. | climate change and the | 38 | 115.454 |
| 18. | environment forest and climate | 38 | 115.454 |
| 19 | forest and climate change | 38 | 115.454 |
| 20 | climate change trust fund | 36 | 109.378 |
| 21 | united nations framework convention | 36 | 109.378 |
| 22 | intergovernmental panel on climate | 35 | 106.34 |
| 23 | nations framework convention on | 35 | 106.34 |
| 24 | to combat climate change | 33 | 100.263 |
| 25 | caused by climate change | 31 | 94.187 |
| 26 | climate change is a | 30 | 91.148 |
| 27 | prime minister sheikh hasina | 30 | 91.148 |
| 28 | the united nations framework | 30 | 91.148 |
| 29 | on climate change ipcc | 29 | 88.11 |
| 30 | on climate change unfccc | 29 | 88.11 |
| 31 | adverse impacts of climate | 28 | 85.072 |
| 32 | climate change in the | 28 | 85.072 |
| 33 | of the most vulnerable | 28 | 85.072 |
| 34 | to climate change and | 28 | 85.072 |
| 35 | fight against climate change | 27 | 82.033 |
| 36 | the bangladesh climate change | 27 | 82.033 |
| 37 | the government of bangladesh | 27 | 82.033 |
| 38 | bangladesh is one of | 26 | 78.995 |
| 39 | human induced climate change | 26 | 78.995 |
| 40 | the bay of bengal | 26 | 78.995 |
| 41 | affected by climate change | 25 | 75.957 |
| 42 | at the university of | 25 | 75.957 |
| 43 | climate change in bangladesh | 25 | 75.957 |
| 44 | climate change strategy and | 25 | 75.957 |
| 45 | consequences of climate change | 25 | 75.957 |
| 46 | of the united nations | 25 | 75.957 |
| 47 | the adverse impacts of | 25 | 75.957 |
| 48 | bangladesh climate change trust | 24 | 72.919 |
| 49 | change strategy and action | 24 | 72.919 |
| 50 | in the face of | 24 | 72.919 |
| 51 | of the climate change | 24 | 72.919 |
| 52 | strategy and action plan | 24 | 72.919 |
| 53 | bangladesh climate change strategy | 23 | 69.88 |
| 54 | for climate change and | 23 | 69.88 |
| 55 | mujib climate prosperity plan | 23 | 69.88 |
| 56 | on climate change and | 23 | 69.88 |
| 57 | the green climate fund | 23 | 69.88 |
| 58 | the most vulnerable countries | 23 | 69.88 |
| 59 | to the impacts of | 23 | 69.88 |
| 60 | adverse effects of climate | 22 | 66.842 |
| 61 | adapt to climate change | 21 | 63.804 |
| 62 | loss and damage fund | 21 | 63.804 |
| 63 | of climate change is | 21 | 63.804 |
| 64 | of the paris agreement | 21 | 63.804 |
| 65 | the loss and damage | 20 | 60.766 |
| 66 | vulnerable to climate change | 20 | 60.766 |
-
Research ethics: Not applicable.
-
Informed consent: Not applicable.
-
Conflict of interest: The author declares that there is no conflict of interest.
-
Data availability: The author confirms that the data supporting the findings of this study are available within the article.
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Articles in the same Issue
- Frontmatter
- Research Articles
- De-naturalizing the worldviews underlying representations of eco-awareness: a multimodal critical discourse analysis of Indonesian EFL textbooks
- Corpus-assisted ecolinguistics and media framing: mapping climate narratives in Bangladesh
- Towards an emplaced ecolinguistics: a critical engagement with Ecolinguistics and Emplacement
- Understanding the perceived capital of a travel destination: a theme-based corpus analysis of an online destination forum
- “Let me be a bridge”: language brokering among emerging adult Latina professionals in the Midwest
- Research-based persona narratives for targeted health interventions in the elderly population: a narrative review
- Research on dynamic categorization of word meaning: review and prospect
- Book Review
- English nouns since 1150: a typological study
Articles in the same Issue
- Frontmatter
- Research Articles
- De-naturalizing the worldviews underlying representations of eco-awareness: a multimodal critical discourse analysis of Indonesian EFL textbooks
- Corpus-assisted ecolinguistics and media framing: mapping climate narratives in Bangladesh
- Towards an emplaced ecolinguistics: a critical engagement with Ecolinguistics and Emplacement
- Understanding the perceived capital of a travel destination: a theme-based corpus analysis of an online destination forum
- “Let me be a bridge”: language brokering among emerging adult Latina professionals in the Midwest
- Research-based persona narratives for targeted health interventions in the elderly population: a narrative review
- Research on dynamic categorization of word meaning: review and prospect
- Book Review
- English nouns since 1150: a typological study