The arch not the stones: Universal feature theory without universal features
Bibliographic record
Abstract
There is a growing consensus that phonological features are not innate, but rather emerge in the course of acquisition. If features are emergent, we need to explain why they are required at all, and what principles account for the way they function in the phonology. I propose that the learners’ task is to arrive at a set of features that account for the contrasts and the phonological activity in their language. For the content of the features, learners use the available materials relevant to the modality (spoken or signed). Formally, contrasts are governed by an ordered feature hierarchy. The concept of a contrastive hierarchy is an innate part of Universal Grammar, and is the glue that binds phonological representations and makes them appear similar across languages. Examples from the Classical Manchu vowel system show the connection between contrast and phonological activity. I then consider the implications of this approach for the acquisition of phonological representations. The relationship between formal contrastive hierarchies and phonetic substance is illustrated with examples drawn from tone systems in Chinese dialects. Finally, I propose that the contrastive hierarchy has a recursive digital character, like other aspects of the narrow faculty of language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".