Bibliographic record
Abstract
Introduction In section 3.7 I sketched the outlines of a theory of phonology that was distilled from the leading ideas discussed in chapter 3. This theory adopts the Contrastivist Hypothesis, which holds that phonology computes only contrastive features. It determines what the contrastive features in a language are by applying the SDA to a contrastive feature hierarchy for that language. In keeping with the Contrastivist Hypothesis, phonological activity serves as the chief heuristic for determining what the feature hierarchy is for a given language. Though the ingredients for such a theory were in place by the 1930s, phonological theory did not develop in this direction; why it did not was the subject of chapters 4–6. These chapters show that the theory of section 3.7 has never properly been put to the test. In this chapter I argue that these ideas remain viable and indispensable to an explanatory theory of phonology. Of course, any contemporary effort to implement such a theory must take account of advances in phonology since the 1930s. For example, the diagnostic given in (38d) of chapter 3, that a contrastive feature must be present in all the allophones of a phoneme, is not consistent with the generative phonological conception that phonology is relatively abstract with respect to phonetics. In keeping with Chomsky and Halle's arguments against taxonomic phonemics, it is unlikely that we can put limits on the degree to which a segment may be modified in the course of a derivation.
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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 | 0.001 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".