Learnability of indexed constraint analyses of phonological opacity
Bibliographic record
Abstract
This paper explores the learnability of indexed constraint (Pater, 2000) analyses of opacity based on the case study of raising in Canadian English (Chomsky, 1964; Chambers, 1973). Such analyses, while avoiding multiple levels of derivation or representation, require the learner to induce indexed constraints, connect these constraints to particular segments in the lexicon, and rank these constraints. An implementation of Round’s (2017) learner for indexed constraints, which is an extension of Biased Constraint Demotion (Prince and Tesar, 2004), is used here to test whether a simple learner can rise to this challenge and learn a restrictive analysis of the opaque pattern (i.e., one that restricts raising to its proper phonological context). Three different datasets are used with decreasing evidence for a restrictive analysis, as well as three underlying form hypotheses (two of which entail entertaining multiple underlying forms for the same surface form simultaneously), with decreasing evidence for the phonotactic patterns in the data (cf. Jarosz, 2006). It is found that the learner can find a restrictive analysis of opaque raising in Canadian English, provided that the most informative dataset is used and multiple underlying forms are considered for those data points that contain [t, d, ɾ] after a diphthong.
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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| 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".