Bibliographic record
Abstract
Abstract This paper argues for quantity-sensitive, trochaic foot structure in Québécois French, which allows for a unified prosodic account of the variable distribution of tenseness of high vowels in non-final syllables. Following Montreuil (Montreuil, Jean-Pierre. 2004a. Fragmenting weight in Scottish English. In Monica Pulki (ed.), La tribune internationale des langues vivantes , 36, 114–22. Paris. Montreuil, Jean-Pierre. 2004b. The Computation of weight in English and in Québec French. First PAC Workshop 23–24 April 2004, Université de Toulouse le Mirail.) a grammatical, sonority-based surface weight distinction is assumed for Québécois French vowels, with tense high vowels associated to a full mora µ, while lax high vowels are associated to a hypomora λ, a weight value less than µ. The weight is shown to be regulated at the level of the minimally monomoraic foot, which can be expanded to include an adjacent syllable in words consisting of more than two syllables, following the proposed Trochaic Markedness Hierarchy, based on the following three ranked principles: 1) quantitative minimum: light and heavy rimes are preferred to superlight (λ) rimes, 2) quantitative evenness: even trochees are preferred to uneven trochees, and 3) quantitative dominance: the left branch that is heavier than the right branch is preferred to the left branch that is lighter. Possible shapes of the trochee are shown to be aligned with alternating surface realizations of high vowels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".