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
Many generalizations are impossible to learn via primary linguistic data, so they are assumed to be part of our genetic endowment. Generativists have tried to reduce Universal Grammar (UG) to a minimum, in particular by appealing to computational efficiency. In principle, this is an important improvement. The bottom line, however, is how well this computational approach explains the data. Unfortunately, it does not. Thus current analyses of subject–AUX inversion still appeal implicitly to several UG constraints in addition to structure dependence. Moreover, this fails empirically even in the wildest cases, such as forming questions by reversing the word order of a declarative. Fortunately, there is a way out of this impasse. Learners realize that different orders of constituents correlate with different meanings. Generating Tense in Comp compositionally derives a polar interrogative interpretation. The logically prior properties of the perceptual and conceptual systems impose constraints that are sufficient to explain language acquisition.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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