MétaCan
Menu
Back to cohort
Record W4251368453 · doi:10.1017/s0008413100000876

On the Learning of Auxiliary Use in the Referential Variety by Speakers of New Brunswick Acadian French

2008· article· en· W4251368453 on OpenAlexaffabout
Patricia Balcom

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsTask (project management)LinguisticsVariety (cybernetics)FrenchPsychologyAP French LanguageComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This study investigates the learning of Referential French by speakers of Acadian French at the university level. One difference between the two varieties lies in their use of auxiliaries in compound tenses. In Acadian French,avoiris used categorically in compound tenses with verbs of inherently directed motion and pronominal verbs, while Referential French usesêtre. A controlled-production task and an acceptability judgment task were administered to 80 speakers of New Brunswick Acadian French who were students at a francophone university in New Brunswick, 40 first-year students and 40 fourth-year students. Results show that, while there is still variability in the fourth-year students’ auxiliary use, their performance is significantly closer to Referential French than that of the first-year students.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.865
Threshold uncertainty score0.269

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.228
Teacher spread0.193 · 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

Citations1
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207