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Record W3180201771 · doi:10.33137/twpl.v43i1.35934

“What do they say in Quebec?”

2021· article· en· W3180201771 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueToronto Working Papers in Linguistics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGender Studies in Language
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNeologismSpellingLinguisticsVariety (cybernetics)PsychologyPoint (geometry)SociologySocial psychologyComputer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper examines how non-binary French-speakers in Quebec express their gender identities in speech. I argue that reformist efforts regarding neutral French should include increased attention to how neutral French is done in informal spoken Quebec French, as I examine how current recommendations based on spelling can fail to be taken up in speech, and how regional varieties can sometimes require different prescriptions. Based on a preliminary field study with eight participants who are part of this community of practice, I find that participants did not use any audible neologisms, such as the ones recommended for writing and for other varieties. Not only did they all use gendered language to refer to non-binary referents, although at a much lower frequency than for binary referents, but they also used gender-avoidance strategies in most cases. I also show that third person clitics seem to be the word category most resistant to neutralization or avoidance for speakers of this variety. I argue that these results point to the development of two distinct systems of neutral French, one for speech and one for writing.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.317
Teacher spread0.294 · 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