Bibliographic record
Abstract
I am grateful to Professor 1 Jordan for his kind words and for his comments on my work, and also to the editors of this journal for inviting me to respond.Professor Jordan makes a point of emphasizing love's sensitivity to the particularities of its object.I think this particularities point is a good one.I'm saddened, however, that I must infer from it, together with what's in the rest of his article, that Professor Jordan did not love my book.For he does not address many of its particularities, instead focusing on what -despite my not using the word -he sees as its emphasis on the impartiality of love, which emphasis appears to him to make the hiddenness argument vulnerable to an approach he wrote about in 2012. 2 Now, it could be that it's not just because he thinks he has this knockdown counterargument but because Professor Jordan thinks very little in the new book is really new that he discloses to the reader so little of its contents.But in that case I would note that other reviewers, looking closer, have thought differently.3
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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 | 0.010 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.036 | 0.072 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".