Testing lexical specificity hypotheses in world Englishes: evidence from the null subject alternation

Iván Tamaredo

doi:10.1515/flin-2025-0073

Article

Testing lexical specificity hypotheses in world Englishes: evidence from the null subject alternation

Iván Tamaredo

Published/Copyright: October 7, 2025

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From the journal Folia Linguistica

Abstract

The present paper tests a series of hypotheses dealing with variation, across dialects of English, in the degree of lexical specificity of constructions. The study focuses on the null subject alternation, which is conceptualized here as consisting of two allostructions, the null and overt variants, of a more schematic constructeme. Five hypotheses put forward in the specialized literature are adapted to the null subject alternation and tested on a dataset of null and overt subjects extracted from GloWbE by means of five indexes of frequency and lexical specificity. The results confirm four of the five hypotheses and provide further support to the claim that speakers of varieties of English in earlier phases in Schneider’s Dynamic Model rely more on partially lexically filled constructions.

Keywords: lexical specificity; null subjects; allostructions; world Englishes; dynamic model

Corresponding author: Iván Tamaredo, Departamento de Estudios Ingleses: Lingüística y Literatura, Universidad Complutense de Madrid, Plaza Menéndez Pelayo s/n, Madrid, 28040, Spain, E-mail: itamared@ucm.es

Funding source: Spanish Ministry of Science, Innovation and Universities

Award Identifier / Grant number: PID2023-146887NB-I00

Acknowledgments

I am grateful to two anonymous reviewers for their helpful and constructive comments on an earlier version of the paper.

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The author states no conflict of interest.
Research funding: This research was conducted with the financial support of the Spanish Ministry of Science, Innovation and Universities (grant PID2023-146887NB-I00).
Data availability: The raw data can be obtained on request from the corresponding author.

Appendix

This appendix contains more detailed information about the indexes of productivity, prototypical fillers, allostructional asymmetry, and probabilistic indigenization. In particular, the data used to calculate the indexes is presented and briefly discussed.

A. Productivity index

As mentioned in Section 2.2, the index of productivity is calculated on the basis of LNRE models computed for each allostruction and phase (cf. Table A1 for goodness-of-fit statistics of these models). Using the results of finite Zipft-Mandelbrot LNRE models, and considering the different sizes of the subcorpora examined, the number of different verb types used with each allostruction is predicted at different sample sizes, up to 1,500 tokens (cf. Figure A1). This threshold was established because the largest sample in the data is that of Phase 5, with 1,186 and 1,119 null and overt subjects, respectively. The productivity index is then the result of dividing the number of verb types of the null subject allostruction, predicted at a size of 1,500 tokens, by the number of predicted verb types of the overt subject allostruction (cf. Table A2).

Table A1:

Goodness-of-fit statistics of LNRE models.

	Phase 2		Phase 3		Phase 4		Phase 5
	Null	Overt	Null	Overt	Null	Overt	Null	Overt
χ ²	3.731	7.502	4.038	1.658	1.750	6.200	2.072	4.942
df	3	3	3	3	3	3	3	4
p	0.292	0.058	0.257	0.646	0.626	0.102	0.557	0.293

Figure A1:

Verb types per phase of the null (left) and overt (right) subject allostructions; vertical lines indicate observed sample sizes; shaded areas around lines represent confidence intervals.

Table A2:

Predicted verb types at a sample size of 1,500 tokens of the null and overt subject allostructions per phase.

	Phase 2	Phase 3	Phase 4	Phase 5
Null subjects	151.95	142.30	118.87	121.00
Overt subjects	460.18	375.40	381.81	403.66
Productivity index	0.33	0.38	0.31	0.30

B. Prototypical fillers index

The prototypical fillers index is calculated on the basis of the results of DCA. In the present study, DCA was used to uncover the verb lemmas that are significantly associated with each of the allostructions of the null subject alternation per phase of development (cf. Tables A3–A6). Then, for each allostruction in each phase, the percentage of significant verb lemmas is calculated out of the total number of verb lemmas used in the verb slot of the allostruction. Finally, the index is calculated by dividing the percentage of significant verb lemmas of the null subject allostruction by the percentage of significant verb lemmas of the overt variant (cf. Table A7).

Table A3:

Significant verb lemmas of the null and overt subject allostructions in phase 2 varieties.

Lemma	Obs. freq. null	Obs. freq. overt	Exp. freq. null	Exp. freq. overt	Preference	Coll. strength	Significance
Sound	22	0	5.752	16.248	Null	13.275	***
Make	19	10	7.583	21.417	Null	5.228	***
Seem	10	2	3.138	8.862	Null	4.317	***
Include	6	1	1.830	5.170	Null	2.786	**
Look	9	5	3.661	10.339	Null	2.565	**
Depend	4	0	1.046	2.954	Null	2.342	**
Take	5	2	1.830	5.170	Null	1.817	*
Provide	3	0	0.784	2.216	Null	1.753	*
Happen	4	1	1.307	3.693	Null	1.743	*
Mean	7	6	3.399	9.601	Null	1.530	*
Say	5	62	17.518	49.482	Overt	4.280	***
Ask	0	11	2.876	8.124	Overt	1.461	*

Significance levels are as follows: ‘***’ equals p < 0.001, ‘**’ stands for p < 0.01, and ‘*’ means p < 0.05.

Table A4:

Significant verb lemmas of the null and overt subject allostructions in phase 3 varieties.

Lemma	Obs. freq. null	Obs. freq. overt	Exp. freq. null	Exp. freq. overt	Preference	Coll. strength	Significance
Sound	57	2	22.458	36.542	Null	22.149	***
Remind	23	1	9.135	14.865	Null	8.625	***
Include	19	3	8.374	13.626	Null	5.459	***
Make	39	20	22.458	36.542	Null	5.164	***
Require	5	0	1.903	3.097	Null	2.104	**
Happen	8	2	3.806	6.194	Null	2.073	**
Return	8	2	3.806	6.194	Null	2.073	**
Contain	7	2	3.426	5.574	Null	1.740	*
Look	13	8	7.994	13.006	Null	1.660	*
Support	6	2	3.045	4.955	Null	1.417	*
Help	11	7	6.852	11.148	Null	1.410	*
Say	5	69	28.168	45.832	Overt	9.674	***
Tell	2	16	6.852	11.148	Overt	1.920	*
Ask	0	8	3.045	4.955	Overt	1.671	*
Offer	0	7	2.665	4.335	Overt	1.462	*

Significance levels are as follows: ‘***’ equals p < 0.001, ‘**’ stands for p < 0.01, and ‘*’ means p < 0.05.

Table A5:

Significant verb lemmas of the null and overt subject allostructions in phase 4 varieties.

Lemma	Obs. freq. null	Obs. freq. overt	Exp. freq. null	Exp. freq. overt	Preference	Coll. strength	Significance
Sound	44	2	19.361	26.639	Null	14.864	***
Make	45	7	21.886	30.114	Null	11.182	***
Seem	13	0	5.472	7.528	Null	4.959	***
Remind	14	2	6.734	9.266	Null	3.684	***
Bring	6	0	2.525	3.475	Null	2.269	**
Represent	5	0	2.104	2.896	Null	1.888	*
Save	4	0	1.684	2.316	Null	1.509	*
Include	7	2	3.788	5.212	Null	1.483	*
Say	6	33	16.415	22.585	Overt	3.612	***
Tell	3	19	9.26	12.74	Overt	2.409	**
Ask	0	8	3.367	4.633	Overt	1.912	*
Know	0	6	2.525	3.475	Overt	1.431	*

Significance levels are as follows: ‘***’ equals p < 0.001, ‘**’ stands for p < 0.01, and ‘*’ means p < 0.05.

Table A6:

Significant verb lemmas of the null and overt subject allostructions in phase 5 varieties.

Lemma	Obs. freq. null	Obs. freq. overt	Exp. freq. null	Exp. freq. overt	Preference	Coll. strength	Significance
Sound	180	11	98.276	92.724	Null	40.736	***
Make	178	25	104.45	98.55	Null	29.543	***
Seem	113	17	66.889	63.111	Null	17.804	***
Include	56	3	30.357	28.643	Null	12.816	***
Depend	23	1	12.349	11.651	Null	5.594	***
Look	65	22	44.764	42.236	Null	5.284	***
Remind	30	4	17.494	16.506	Null	5.244	***
Give	35	16	26.241	24.759	Null	2.043	**
Save	9	1	5.145	4.855	Null	1.872	*
Allow	15	5	10.291	9.709	Null	1.560	*
Help	21	10	15.951	15.049	Null	1.312	*
Say	45	93	71.006	66.994	Overt	5.496	***
Ask	0	13	6.689	6.311	Overt	4.096	***
Tell	8	25	16.98	16.02	Overt	2.903	**
Think	1	11	6.174	5.826	Overt	2.638	**
Mean	2	12	7.203	6.797	Overt	2.331	**
Know	6	19	12.863	12.137	Overt	2.330	**
Cite	0	7	3.602	3.398	Overt	2.201	**
Play	2	11	6.689	6.311	Overt	2.077	**
Feature	0	6	3.087	2.913	Overt	1.886	*
Involve	0	6	3.087	2.913	Overt	1.886	*
Live	0	6	3.087	2.913	Overt	1.886	*
Point	0	6	3.087	2.913	Overt	1.886	*
State	0	6	3.087	2.913	Overt	1.886	*
Offer	0	5	2.573	2.427	Overt	1.571	*
Remain	0	5	2.573	2.427	Overt	1.571	*
Stop	0	5	2.573	2.427	Overt	1.571	*
Speak	2	8	5.145	4.855	Overt	1.348	*
Get	10	19	14.921	14.079	Overt	1.313	*

Significance levels are as follows: ‘***’ equals p < 0.001, ‘**’ stands for p < 0.01, and ‘*’ means p < 0.05.

Table A7:

Percentage of significant verb lemmas per phase.

	Phase 2	Phase 3	Phase 4	Phase 5
N of significant lemmas – null	10	11	8	11
Total n of lemmas – null	57	97	69	114
% Of significant lemmas – null	17.54	11.34	11.59	9.65
N of significant lemmas – overt	2	4	4	18
Total n of lemmas – overt	220	250	174	348
% Of significant lemmas – overt	0.91	1.60	2.30	5.17
Prototypical fillers index	19.27	7.09	5.04	1.87

C. Allostructional asymmetry index

The allostructional asymmetry index is calculated by dividing the percentage of verb lemmas shared by both allostructions, out of the total number of verb lemmas in the null subject allostruction, by the percentage of shared verb lemmas in the overt allostruction (cf. Table A8).

Table A8:

Percentage of shared verb lemmas out of the total number of verb lemmas of the null and overt subject allostructions per phase.

	Phase 2	Phase 3	Phase 4	Phase 5
N of shared lemmas	39	79	49	101
Total n of lemmas – null	57	97	69	114
% Of shared lemmas – null	68.42	81.44	71.01	88.60
Total n of lemmas – overt	220	250	174	348
% Of shared lemmas – overt	17.73	31.60	28.16	29.02
All. asymmetry index	3.86	2.58	2.52	3.05

D. Probabilistic indigenization index

The probabilistic indigenization index is calculated on the basis of the results of VADIS, a method to measure the degree of grammatical dissimilarity between varieties of English developed by Szmrecsanyi and colleagues (Szmrecsanyi et al. 2019; Szmrecsanyi and Grafmiller 2023). As mentioned in Section 2.2, VADIS examines probabilistic differences in the grammars of varieties of English along three lines of evidence: (i) Do the same variables have a statistically significant effect across varieties? (ii) Are probabilistic constraints similar with respect to the size of their effects across varieties? (iii) Do the constraints have the same relative importance in all the varieties considered?

VADIS is carried out in three steps. First, a mixed-effects binary logistic regression model (e.g., Baayen 2008: Ch. 7) is calculated for each phase using the same formula (cf. Tables A9–A13); in the present study, the allostruction (overt vs. null) was the response variable, the constraints in Table 1 were included as fixed predictors, while verb lemma was the only random predictor in the models. Second, a score reflecting the dissimilarities between phases is calculated for each of the three lines of evidence. The first score, statistical significance, is derived from the number of significant and non-significant constraints shared by the phases. The second score, effect size, is computed as the difference between the coefficient estimates in the per-phase mixed-effects models. The third score, constraint ranking, is computed from the Spearman’s rank correlation coefficients between the factor’s variable importance values. Finally, a mean dissimilarity score is calculated per phase by averaging over the three scores (cf. Table A14). This dissimilarity score, which reflects how different the probabilistic grammar of one phase is from the rest, constitutes the probabilistic indigenization index.

Table A9:

Data for mixed-effects models.

Constraint		Overt		Null		Total
Constraint		N	%	N	%
Phase	Phase 2	483	73.85	171	26.15	654
	Phase 3	672	61.94	413	38.06	1,085
	Phase 4	377	57.91	274	42.09	651
	Phase 5	1,119	48.55	1,186	51.54	2,305
V. Semantics	Activity	990	56.15	773	43.85	1,763
	Aspectual	102	60.00	68	40.00	170
	Causative	73	47.40	81	52.60	154
	Communication	713	85.39	122	14.61	835
	Existence	252	35.95	449	64.05	701
	Psychological	433	46.76	493	53.24	926
	Simple occ.	88	60.27	58	39.73	146
Tense	Present	1,092	39.15	1,697	60.85	2,789
	Past	1,559	81.79	347	18.21	1,906
R. Continuity	Full	678	45.75	804	54.25	1,482
	Maintenance	1,449	67.96	683	32.04	2,132
	Partial	524	48.47	557	51.53	1,081
Persistence	Other	1,361	58.69	958	41.31	2,319
	Pronoun	983	58.86	687	41.14	1,670
	Null	307	43.48	399	56.52	706
Pronoun	It	821	33.48	1,631	66.52	2,452
	S/he	1,830	81.59	413	18.41	2,243
Total		2,651	56.46	2,044	43.54	4,695

Table A10:

Results and goodness-of-fit statistics of the phase 2 model.

Fixed effects
Predictor	Estimate	Standard error	z	p
Intercept	−0.228	0.449	−0.509	0.611
Verb semantics: aspectual	−0.571	0.838	−0.681	0.496
Verb semantics: causative	−1.590	1.183	−1.344	0.179
Verb semantics: communication	−1.329	0.562	−2.363	0.018
Verb semantics: existence	0.222	0.569	0.390	0.697
Verb semantics: psychological	−0.498	0.539	−0.924	0.356
Verb semantics: simple occ.	0.006	0.779	0.008	0.994
Tense: past	−1.496	0.323	−4.637	>0.001
Ref. continuity: maintenance	−0.634	0.318	−1.992	0.046
Ref. continuity: partial	−0.322	0.373	−0.864	0.388
Persistence: pronoun	0.226	0.292	0.773	0.439
Persistence: null	1.421	0.377	3.764	>0.001
Pronoun: S/he	−1.128	0.345	−3.266	0.001

Random effects

Predictor		Standard deviation
Verb lemma		1.372

Goodness-of-fit

C		0.925
Accuracy		86.85 %

Table A11:

Results and goodness-of-fit statistics of the phase 3 model.

Fixed effects
Predictor	Estimate	Standard error	z	p
Intercept	0.592	0.284	2.087	0.037
Verb semantics: aspectual	0.880	0.667	1.321	0.186
Verb semantics: causative	0.316	0.689	0.458	0.647
Verb semantics: communication	−1.231	0.427	−2.880	0.004
Verb semantics: existence	0.152	0.408	0.372	0.710
Verb semantics: psychological	−0.276	0.356	−0.776	0.438
Verb semantics: simple occ.	0.981	0.665	1.475	0.140
Tense: past	−1.504	0.220	−6.850	>0.001
Ref. continuity: maintenance	−0.946	0.216	−4.373	>0.001
Ref. continuity: partial	−0.243	0.264	−0.919	0.358
Persistence: pronoun	−0.478	0.205	−2.326	0.020
Persistence: null	1.528	0.259	5.910	>0.001
Pronoun: S/he	−1.024	0.219	−4.682	>0.001

Random effects

Predictor		Standard deviation
Verb lemma		1.066

Goodness-of-fit

C		0.907
Accuracy		84.70 %

Table A12:

Results and goodness-of-fit statistics of the phase 4 model.

Fixed effects
Predictor	Estimate	Standard error	z	p
Intercept	0.409	0.377	1.085	0.278
Verb semantics: aspectual	0.245	0.810	0.302	0.762
Verb semantics: causative	−0.805	0.802	−1.003	0.316
Verb semantics: communication	−0.523	0.527	−0.993	0.321
Verb semantics: existence	0.380	0.488	0.777	0.437
Verb semantics: psychological	0.165	0.464	0.356	0.722
Verb semantics: simple occ.	0.243	0.816	0.298	0.766
Tense: past	−0.836	0.298	−2.809	0.005
Ref. continuity: maintenance	−0.364	0.297	−1.226	0.220
Ref. continuity: partial	0.082	0.326	0.251	0.802
Persistence: pronoun	0.209	0.276	0.757	0.449
Persistence: null	1.191	0.369	3.227	0.001
Pronoun: S/he	−2.597	0.304	−8.542	>0.001

Random effects

Predictor		Standard deviation
Verb lemma		1.083

Goodness-of-fit

C		0.927
Accuracy		86.79 %

Table A13:

Results and goodness-of-fit statistics of the phase 5 model.

Fixed effects
Predictor	Estimate	Standard error	z	p
Intercept	−0.556	0.279	−1.993	0.046
Verb semantics: aspectual	1.386	0.616	2.249	0.025
Verb semantics: causative	0.395	0.700	0.565	0.572
Verb semantics: Communication	−1.153	0.419	−2.752	0.006
Verb semantics: existence	0.332	0.409	0.811	0.417
Verb semantics: psychological	0.218	0.354	0.617	0.537
Verb semantics: simple occ.	0.497	0.638	0.780	0.436
Tense: past	−1.136	0.141	−8.045	>0.001
Ref. continuity: maintenance	−0.271	0.145	−1.874	0.061
Ref. continuity: partial	−0.139	0.157	−0.885	0.376
Persistence: pronoun	0.101	0.130	0.783	0.434
Persistence: null	0.823	0.175	4.696	>0.001
Pronoun: S/he	−1.032	0.152	−6.812	>0.001

Random effects

Predictor		Standard deviation
Verb lemma		1.494

Goodness-of-fit

C		0.902
Accuracy		82.30 %

Table A14:

Dissimilarity scores.

	Phase 2	Phase 3	Phase 4	Phase 5
Statistical significance	0.14	0.19	0.19	0.19
Effect strength	0.76	0.57	0.63	0.60
Constraint ranking	0.19	0.27	0.34	0.19
Prob. indigenization index	0.36	0.34	0.39	0.33

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Received: 2025-04-18

Accepted: 2025-09-15

Published Online: 2025-10-07

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https://doi.org/10.1515/flin-2025-0073

Keywords for this article

lexical specificity; null subjects; allostructions; world Englishes; dynamic model