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Record W4413894894 · doi:10.5565/rev/isogloss.491

Grammatical gender in Zapotec/Spanish monolingual and bilingual speakers

2025· article· en· W4413894894 on OpenAlexafffund
Gladys Cruz Mendoza, Juana M. Liceras, Ibán Mañas Navarrete

Bibliographic record

VenueIsogloss Open Journal of Romance Linguistics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of CanadaMinisterio de Ciencia e InnovaciónMinisterio de Ciencia, Innovación y UniversidadesUniversity of Ottawa
KeywordsLinguisticsGrammatical genderPsychologyNeuroscience of multilingualismPhilosophyNoun

Abstract

fetched live from OpenAlex

As inanimate nouns are not classified according to grammatical gender in Zapotec, it is legitimate to wonder whether it is possible that the influence of Zapotec on Spanish plays a role in the quantity and in the grammatical and structural contexts where pronoun “lo” is used as a substitute for “la”. We also address whether or not there are differences between the Zapotec/Spanish bilinguals and the Spanish monolinguals, as Spanish monolinguals have been in contact with Zapotec/Spanish bilinguals for generations. We have analyzed data from 40 adults, 20 Spanish monolinguals and 20 Zapotec/Spanish bilinguals in San Cristóbal Lachirioag, Oaxaca, Mexico. The data analysis carried out on the three experimental tasks administered to the participants shows differences between the bilingual and the monolingual groups, both in terms of the number of inherent feminine nouns that are pronominalized with "lo" and with respect to the structural contexts where this occurs. Another relevant finding relates to the differences between the first task (classification of nouns according to gender) and the second task (gender agreement), which leads us to propose that the two groups of participants have problems with gender agreement but not with gender assignment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.314
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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