The Typology of Weight in Strict CV Metrics: A Challenge to Moraic Theory
Bibliographic record
Abstract
The problem of light geminates haunts moraic representations of syllable structure. Topintzi and Zimmermann (2025) offer an account within moraic theory and assert that length-based, nonmoraic analyses are at a loss when facing this problem. We use this debate as a starting point to discuss weight hierarchies in the languages of the world. We propose an analysis in Strict CV Metrics (Faust and Ulfsbjorninn 2018), a nonmoraic theory that does not conflate syllable structure and metrics. Instead, the latter is built on the former. The typology of weight takes the shape of a restrictive, nonrerankable parameter hierarchy, which indicates what aspects of universal syllabification contribute weight in a given language. The parameter hierarchy correctly predicts all the attested weight scales in natural language and correctly excludes many unattested systems. We compare our approach with the moraic one and find it preferable on several fronts. Unlike its competitor, it does not require arbitrary phonetic translations; it is easily extendable to long vowels; and it makes clear, correct, and falsifiable predictions.
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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".