Hiatus and Hiatus Resolution in Québécois French
Bibliographic record
Abstract
This thesis is about vowel-vowel sequences across word boundaries in Québécois French (QF). QF has a number of phonological processes that seem motivated by hiatus avoidance, yet hiatus is tolerated in many instances as well. This research is about why hiatus is tolerated in QF, why it is avoided, and what grammatical models can account for the relevant processes.\nMy work is intended as a contribution to the study of how best to account for variation and opacity within current grammatical models, and as a contribution to the study of the QF vowel system. The data are drawn from a corpus constructed from recordings of web extras for a Québécois reality television series. The data primarily come from a single speaker to ensure that any variation in the data truly represents intra-grammar variation, but data from other speakers are used as safeguard. Through the use of quantitative data as a means of investigating problems in theoretical phonology, the thesis is also meant to contribute to methodological discussions and discussions about the relationship between phonetics and phonology.\nI propose that the patterns of hiatus and hiatus resolution in QF are best modeled through three sets of constraints organized in a serial manner. This proposal is based on the claims that the data show evidence for an anti-hiatus constraint, for feature-based analysis, for stochastic modeling, and for multiple levels.\nThe proposed model combines insights from Stochastic Optimality Theory (Boersma & Hayes 2001), multi-level Optimality Theory (Kiparsky 2000, 2010; Rubach 2000), and the Contrastivist Hypothesis (Dresher & Rice 2002, Dresher 2009, Hall 2007). Within the model, the first constraint set targets the smallest prosodic constituents and produces categorical outputs, the second applies to intermediate-sized constituents and can model optionality, and the third handles the largest prosodic constituents and produces complex patterns of variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".