The three degrees of metrical strength in Strict CV metrics, a theory without parsing
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
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.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it