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Record W7005304719

Processing of Grammatical Gender in French: an Individual Differences Study

2023· dissertation· en· W7005304719 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersMcMaster University
KeywordsGrammatical genderReading (process)Affect (linguistics)NounControl (management)FluencySyntaxCognitionVariation (astronomy)
DOInot available

Abstract

fetched live from OpenAlex

Past studies of grammatical gender have shown that native speakers encounter processing difficulties when encountering a form that does not agree in gender with previous words. However, the specific behavioral and neural responses to these difficulties have not been replicated across studies of the same type. This is in part due to different experimental designs and statistical analyses, but a crucial factor may be the lack of control between nouns of masculine and feminine gender in stimuli creation. Masculine and feminine gender show distinct distributional asymmetries and collapsing them into one condition diminishes the explanatory power of any study examining grammatical gender. We used reading times in a self-paced reading experiment to examine whether masculine and feminine gender violations differentially affect processing speeds. Fifty French speakers read sentences that were well-formed or contained a mismatch in gender between determiner and noun, half of which were masculine and half feminine. Following Beatty-Martínez et al. (2021), we added individual difference measures to determine how participant-specific factors modulate processing. Participants also completed a category verbal fluency task and the AX-CPT, a measure of cognitive control. They found that ERP components were modulated by these components for Spanish speakers and the modulation differed between masculine noun and feminine noun violations. We hypothesized that reading times would be similarly affected in French, a closely related language with the same gender categories. However, no conditions or interactions reached statistical significance. It is unclear whether this is due to the experimental manipulation or lack of control for participants’ language background, as we had a high number of bilingual and multilingual participants. Regardless, elements of the procedure may provide insight on how to design future experiments that lay a groundwork in understanding the most basic elements of gender processing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.286
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

Explore more

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