A “pragmatic ” account of intervention effects Paul Hagstrom
Bibliographic record
Abstract
The second meeting of the McGill whenthusiasts, Winter 2008. 1 The pragmatic proposal from Tomioka (2007) 1.1 Movement-based analyses of LF intervention effects Tomioka reviews the basic “intervention ” paradigms again, but at the same time makes the point that there are differences in the magnitude of the acceptability degradations. I’ll continue to stick to the Japanese examples, and just give the degraded ones. If the first two phrases are scrambled, the sentences are good, and Tomioka also gives the Korean analogs as well. (1) a.? * daremo anyone nani-o what-acc yom-ana-katta-no? read-neg-past-Q (‘What did no one read?’) b.? * John-sika John-except nani-o what-acc yom-ana-katta-no? read-neg-past-Q (‘What did no one but John read?’) c.?? daremo-ga everyone-nom nani-o what-acc yon-da-no? read-past-Q (‘What did everyone read?’) d.?? dareka-ga someone-nom nani-o what-acc yon-da-no? read-past-Q (‘What did someone read?’) e.??? [ John-ka
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".