Bibliographic record
Abstract
A book that has taken so long to be born accumulates many debts in utero.For the inspiration to begin this journey, which has introduced me to new areas of scholarship and has changed how I think about the world, I will always be grateful to the marvelous collective of interdisciplinary scholars that met for some years on Friday afternoons in Glebe.There we read and drank and discussed; I was first introduced to Levinas there and first encouraged to take up the idea for this book.To Robyn Ferrell, who jointly convened the Program for Judgment and Expression with me, and to Sue Best, Andrew Murphy, Paul Patton, Colin Perrin, Nick Smith, Nicholas Strobbe, and the passing parade, thanks for an utterly irreplaceable experience of academic friendship and collegiality.For the enthusiasm to sustain this work even as difficulties mounted and time meandered, I am indebted to those many colleagues and academic friends who read and commented on various chapters, drafts, and papers.I have in mind Peter Cane and
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.573 | 0.358 |
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".