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A quantitative approach to sociotopography in Austronesian languages

  • Leah Pappas ORCID logo EMAIL logo and Gary Holton ORCID logo
Published/Copyright: January 20, 2022
Linguistics Vanguard
From the journal Linguistics Vanguard Volume 8 Issue s1

Abstract

Absolute spatial orientation systems are pervasive and diverse among Austronesian languages, and decades of research has suggested that such systems are motivated at least in part by environmental and cultural factors. In this paper, we take a quantitative approach to the study of orientation systems by presenting the results of an exploratory multifactorial analysis of spatial orientation systems across 131 Austronesian languages, representing nearly all available data on orientation systems for the family. We analyze these data using multinomial logistic regression to uncover correlations between orientation type and four predictor variables representing cultural and environmental factors: geographic distribution, economy, geography (proximity to the sea), and ruggedness of terrain. Our model suggests that while not entirely predictive of the type of orientation system, the factors geography and economy alone account for much of the variation among spatial orientation systems in our sample, supporting a “weak” form of the Sociotopographic Model (Palmer, Bill, Jonathon Lum, Jonathan Schlossberg & Alice Gaby. 2017. How does the environment shape spatial language? Evidence for sociotopography. Linguistic Typology 21(3). 457–491). Additionally, this study demonstrates the potential of quantitative analytical methods for exploring the relationship between culture, environment, and spatial orientation systems.

Keywords: Austronesian; sociotopography; spatial orientation
Corresponding author: Leah Pappas, University of Hawai’i at Mānoa, Honolulu, Hawaii, USA, E-mail:

Funding source: US National Science Foundation 10.13039/100000001

Award Identifier / Grant number: 1761223

Award Identifier / Grant number: 2025315

Acknowledgment

This paper benefited enormously from the feedback of two anonymous reviewers, as well as discussions with the volume editors. Any remaining errors or misrepresentations are solely the responsibility of the authors.

  1. Research funding: This paper was supported in part by US National Science Foundation grants 1761223 and 2025315 (DOI: 10.13039/100000001).

Appendix: Additional data

Tables 4 and 5 present the model results (log odds) using all four predictors, for the combined and doubled methods, respectively. Table 6 presents the values of Akaike’s Information Criterion (AIC) for models run with all subsets of the four predictors. For both methods the lowest AIC, and hence best model fit, occurs with the economy and geography predictors.

Table 4:

Log odds (combined method, all four predictors).

Orientation type
Cardinal Coastal Elevation Land–sea* Riverine Land–sea + cardinal Land–sea + elevation Riverine + cardinal
Island −0.64 −2.49† 32.66††† −0.9 −0.52 0.86 27.06 −4.63†††
(0.58) (0.05) (0.00) (0.46) (0.78) (0.62) (0.83) (0.00)
Village −0.96 −1.04 32.74††† −0.19 0.22 −39.63 26.54 32.58†††
(0.46) (0.35) (0.00) (0.87) (0.92) (0.00) (0.83) (0.00)
Agriculture −0.71 −1.52 −2.18 −1.71 −1.12 −0.1 −18.83††† −77.28†††
(0.58) (0.34) (0.31) (0.29) (0.58) (0.96) (0.00) (0.00)
Subsistence −1.54 0.93 −2.66 0.42 −1.21 −39.19 12.14 −4.1††
(0.22) (0.48) (0.14) (0.75) (0.52) (0.99) (0.03)
Coast −1.93 −1.28 34.59††† 28.71††† −3.08† −1.97 32.05 10.58†††
(0.13) (0.37) (0.00) (0.00) (0.08) (0.3) (0.9) (0.00)
Inland −32.54††† −26.3 65.13††† 6.3 30.65††† −2.07 2.95††† 42.23†††
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Mountainous −0.88 1.34 0.03 1.85 0.96 0.73 29.0 0.23
(0.36) (0.24) (0.99) (0.15) (0.52) (0.65) (0.91) (0.91)
Intercept 3.48†† 0.99 −66.83††† −29.54††† 0.8 0.31 −100.2 −41.33†††
(0.01) (0.53) (0.00) (0.00) (0.65) (0.88) (0.68) (0.00)

  1. p-values in parentheses. AIC: 363.480, McFadden’s R 2: 0.556. † p < 0.1. †† p < 0.05. ††† p < 0.01.

Table 5:

Log odds (doubled method, all four predictors).

Orientation type
Cardinal Coastal Elevation Land–sea* Riverine
Island −0.34 −1.78† 16.47††† −0.12 −0.00
(0.7) (0.08) (0.00) (0.9) (1.0)
Village 0.04 −0.54 15.65††† 0.1 2.18
(0.97) (0.48) (0.00) (0.91) (0.15)
Agriculture 1.0 0.96 0.24 0.93 0.53
(0.29) (0.49) (0.89) (0.52) (0.68)
Subsistence −1.8† 1.45 −0.76 0.95 −2.15†
(0.08) (0.24) (0.59) (0.44) (0.09)
Coast 1.19 0.92 −16.42††† −15.41††† 1.62
(0.18) (0.43) (0.00) (0.00) (0.23)
Inland −32.54 −3.53††† 16.28††† −3.77††† 18.72†††
(0.99) (0.00) (0.00) (0.00) (0.00)
Mountainous 0.97 −0.85 −0.71 −1.27 0.47
(0.2) (0.39) (0.6) (0.29) (0.67)
Intercept −0.64 −1.07 −16.54††† −1.11 −2.71†
(0.47) (0.36) (0.00) (0.35) (0.09)

  1. p-values in parentheses. AIC: 373.417, McFadden’s R 2: 0.498. † p < 0.1. †† p < 0.05. ††† p < 0.01.

Table 6:

Akaike’s Information Criterion for subsets of the four predictors.

Subset of predictors Combined Doubled
All predictors 363.48 373.42
Predictors excluding distribution 351.50 370.56
Predictors excluding economy 468.97 486.37
Predictors excluding geography 367.73 386.74
Predictors excluding terrain 358.38 369.67
Distribution and economy 367.23 386.90
Distribution and geography 478.25 497.76
Distribution and terrain 516.16 551.83
Economy and geography 347.93 368.84
Economy and terrain 374.52 397.29
Geography and terrain 454.09 479.38
Distribution only 528.05 567.83
Economy only 376.07 399.25
Geography only 473.41 505.37
Terrain only 525.41 564.46

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/lingvan-2020-0044).

Received: 2020-05-21
Accepted: 2021-05-10
Published Online: 2022-01-20

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Diversity in representing space within and between language communities
  4. A quantitative approach to sociotopography in Austronesian languages
  5. Directionals, topography, and cultural construals of landscape in Lamaholot
  6. A socially anchored approach to spatial language in Kalaallisut
  7. River-based and egocentric spatial orientation in Yine
  8. Geocentric directional systems in Australia: a typology
  9. The irrelevance of scale and fixedness in landscape terms in two Australian languages
  10. Changes in spatial frames of reference use in Iwaidja in different intergenerational contexts
  11. Cross-generational differences in linguistic and cognitive spatial frames of reference in Negev Arabic
  12. Sociotopography meets Dialectology: the case of Aquilan
  13. Conflation of spatial reference frames in deaf community sign languages
  14. Linguistic spatial reference systems across domains: How people talk about space in sailing, dancing, and other specialist areas
  15. The influence of language, culture, and environment on the use of spatial referencing in a multilingual context: Taiwan as a test case
  16. Reference frames in language and cognition: cross-population mismatches
  17. From the field into the lab: causal approaches to the evolution of spatial language
https://doi.org/10.1515/lingvan-2020-0044
Keywords for this article
Austronesian; sociotopography; spatial orientation

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Diversity in representing space within and between language communities
  4. A quantitative approach to sociotopography in Austronesian languages
  5. Directionals, topography, and cultural construals of landscape in Lamaholot
  6. A socially anchored approach to spatial language in Kalaallisut
  7. River-based and egocentric spatial orientation in Yine
  8. Geocentric directional systems in Australia: a typology
  9. The irrelevance of scale and fixedness in landscape terms in two Australian languages
  10. Changes in spatial frames of reference use in Iwaidja in different intergenerational contexts
  11. Cross-generational differences in linguistic and cognitive spatial frames of reference in Negev Arabic
  12. Sociotopography meets Dialectology: the case of Aquilan
  13. Conflation of spatial reference frames in deaf community sign languages
  14. Linguistic spatial reference systems across domains: How people talk about space in sailing, dancing, and other specialist areas
  15. The influence of language, culture, and environment on the use of spatial referencing in a multilingual context: Taiwan as a test case
  16. Reference frames in language and cognition: cross-population mismatches
  17. From the field into the lab: causal approaches to the evolution of spatial language
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