Response to David Adger's ‘Remarks on Minimalist feature theory and Move’
Bibliographic record
Abstract
David Adger raises some interesting issues and makes several valuable points in his ‘Remarks on Minimalist feature theory and Move’ (henceforth MFTM), a response to our review article ‘Symptomatic imperfections’ (henceforth SI) in this journal (Asudeh & Toivonen 2006), which was in part a review of his Core syntax (Adger 2003). In this response, we address some of the points in MFTM. We would also like to set the record straight about some points in SI which we feel have been misrepresented. In several instances, MFTM argues against claims that were not made in SI. Whatever the independent merit of these arguments, we do not wish to defend viewpoints we did not propose in the first place.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.029 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".