The three degrees of metrical strength in Strict CV metrics, a theory without parsing
Bibliographic record
Abstract
Abstract Typology establishes three degrees of metrical strength. Foot-based theories designate the intermediate degree as that of unparsed syllables, i.e. syllables that are not part of a foot. However, this denotation of parsing mispredicts massively; moreover, there is no real reason why such unparsed syllables should be of intermediate prosodic strength (as opposed to the weakest or strongest). This paper presents an alternative account in Strict CV metrics (Ulfsbjorninn 2014, Faust & Ulfsbjorninn 2018). The correct three-way hierarchy follows from the basic operation of the theory, namely incorporation , whereby one nucleus becomes prominent by incorporating metrical significance from another nucleus. Examples come first from the more classical cases of Dutch and English and then from three test-cases provided by unrelated languages: St’át’imcets (Lillooet Salish), Burmese, and Tiberian Hebrew. No appeal is made to the notion of parsing.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".