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Record W3022101424 · doi:10.3765/amp.v8i0.4650

Gradient behavior without gradient underlying representations: the case of French liaison

2020· article· en· W3022101424 on OpenAlexaboutno aff
Benjamin Storme

Bibliographic record

VenueProceedings of the Annual Meetings on Phonology · 2020
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsWord (group theory)LinguisticsConstraint (computer-aided design)Representation (politics)Computer sciencePhonologyNatural language processingPsychologyArtificial intelligenceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

French liaison consonants are challenging for phonological theory because they pattern ambiguously between word-initial and word-final consonants. In recent works, these facts have been used to motivate different underlying representations for liaison consonants and non-liaison consonants. This paper argues that this move is not necessary. The gradient behavior of liaison consonants can indeed be derived through constraint interaction while maintaining that liaison consonants and non-liaison consonants have the same underlying representation. Two independently motivated hypotheses will play a key role in deriving this result: (i) word variants strive to be similar to their citation forms via output-output correspondence and (ii) concatenating two words (word 1 and word 2) has phonetic/phonological consequences on word 1's final segment and on word 2's initial segment. Together with the fact that liaison consonants are absent from the citation forms of liaison words, these hypotheses predict that liaison consonants will be less protected against changes than stable word-final consonants but more protected than word-initial consonants, thus explaining their gradient behavior. The analysis is illustrated with a detailed case study on Quebec French affrication combining corpus data and grammatical modeling.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.071
GPT teacher head0.363
Teacher spread0.292 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Annual Meetings on PhonologySame topicPhonetics and Phonology ResearchFrench-language works237,207