Bibliographic record
Abstract
Introduction Chomsky and Halle’s approach to phonological theory, as with other components of generative grammar, represented a sharp break with the main currents of American linguistics that immediately preceded them. The differences were conceptual as well as technical. Accounts of the development of phonology emphasize technical issues, such as arguments over the existence of a “taxonomic phonemic level,” or whether it is permissible to “mix levels” in a phonological analysis. Lying behind discussion of these issues, however, were assumptions about psychology and the practice of science. Indeed, throughout the development of phonology, major changes came about not only through technical breakthroughs, but also by reinterpreting the significance of existing technical devices. This was also the case with Chomsky and Halle’s innovations. In this chapter I discuss Chomsky and Halle’s contributions to phonological theory by putting their views in the context of the theories that prevailed before them. I will also try to connect the technical issues to the larger conceptual ones concerning the nature of language acquisition and the mind. I will be treating Chomsky and Halle’s contributions together, without attempting to distinguish who contributed precisely which ideas. Their early work in generative phonology, culminating in the major work The Sound Pattern of English (Chomsky & Halle 1968, henceforth SPE ), was done jointly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".