Bibliographic record
Abstract
This is a wonderful book that uses careful, data-driven theorizing to motivate an Inferentialist and Non-factualist—although not Expressivist—semantics and theory of content for conditionals. According to Khoo’s theory, conditionals of all types encode (although speakers do not use them to express) inferential dispositions, and environments embedding conditionals are sometimes sensitive to their inferential content. The book is technically masterful; Khoo has a knack for formal elegance. As with Bennett [3], discussions of core technical issues are, in large part, disarmingly straightforward. (Some, it should be said, are more daunting, but diligent readers should generally be able to get the gist.) Khoo’s command of the sprawling and multidisciplinary literature on conditionals is remarkable. A wide array of phenomena about conditionals is brought under the heading of a unified theory. I don’t know an account with broader empirical coverage. Comparison with Bennett shows the degree to which philosophy of language has advanced, both empirically and theoretically, over the last twenty years.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".