Reductio ad discrimen: Where features come from
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>This paper addresses two fundamental questions about the nature of formal features in phonology and morphosyntax: what is their expressive power, and where do they come from? To answer these questions, we begin with the most restrictive possible hypothesis (all features are privative, and are wholly dictated by Universal Grammar, with no room for cross-linguistic variation), and examine the extent to which empirical evidence from a variety of languages compels a retreat from this position. We argue that there is little to be gained by positing a universal set of specific features, and propose instead that the crucial contribution of UG is the language learner's ability to construct features by identifying correlations between contrasts at different levels of linguistic structure. This view resonates with current research on how the interaction between UG and external 'third factors' shapes the structure of language, while at the same time harking back to the Saussurean notion that contrast is the central function of linguistic representations.</p>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it