Bibliographic record
Abstract
Extract Cynthia L. Allen is a Reader at the Australian National University. She has specialized in using data gathered from texts to explore the history of English syntax. Her contributions towards a deeper understanding of the relationship between morphological and syntactic change include her book Case Marking and Reanalysis: Grammatical Relations from Old to Early Modem English (Clarendon Press 1995). Stephen R. Anderson obtained his Ph.D. from MIT and taught at UCLA and the Johns Hopkins University before going to Yale as Professor of Linguistics and Cognitive Science. He has written extensively on all aspects of linguistics and is the author of The Organization of Phonology (Academic Press 1974), Phonology in the Twentieth Century (University of Chicago Press 1985}, A-morphous Morphology (CUP 1992), and (with David Lightfoot) The Language Organ: Linguistics as Cognitive Physiology (CUP 2002). Susana Bejar is a Ph.D. student at the University of Toronto. Her main research interest...
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.688 | 0.551 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".