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Record W2805840442 · doi:10.3390/languages3020018

Acquisition of French Causatives: Parallels to English Passives

2018· article· en· W2805840442 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.

Bibliographic record

VenueLanguages · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsMcGill University
FundersUniversity of ConnecticutNational Science Foundation
KeywordsTransitive relationCausativeLinguisticsParallelsModal verbPhilosophyMathematicsVerbCombinatoricsEngineering

Abstract

fetched live from OpenAlex

Guasti (2016) notes similarities between English get- and be-passives, and Romance causatives of the faire-par and faire-infinitif types, respectively. On this basis she conjectures that faire-infinitif will show an acquisitional delay similar to that found for English be-passives, which are not mastered until sometime after the age of four. Here, this prediction is tested and supported for French faire-infinitif causatives of transitive verbs. To explain the delay, the Universal Freezing Hypothesis (UFH) of Snyder and Hyams (2015) is extended to this type of causative: a restriction on movement is recast as a restriction on AGREE. A novel prediction, that faire causatives involving unergative or unaccusative verbs will be acquired much earlier, is also tested and supported. Finally, English get-passives and French “reflexive causative passives” are examined in light of the fact that both are acquired substantially earlier than age four.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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