Bibliographic record
Abstract
The project on which this book is based has now spanned almost a decade.In this time, I have relied on countless individuals and various modes of support, from the intellectual to the emotional to the very practical -none of which can be stressed enough.My family, friends, and colleagues have all contributed to seeing me through this work and to making it what it is.I especially thank Sherene Razack, who thought she was finished with me after my dissertation, for her ongoing interest and encouragement to publish.I have continued to draw upon her passion and her brilliantly incisive critical gaze while making revisions.The early invaluable contributions from my dissertation committee have also seen their way well into the final work.The thoughtful engagement shown by Ruth Roach Pearson, Kari Dehli, and Nicholas Blomley offered insight and wisdom well beyond the call.This research was supported by a four-year fellowship from the Social Sciences and Humanities Research Council of Canada and I remain very grateful for their support.While it is standard to thank one's peer referees, I truly cannot say enough about mine.I owe much to the three anonymous reviewers who took the time to engage in such depth with this work.All were respectful and supportive while offering the most constructive criticism.I felt I was in conversation with them as I completed the manuscript.My editor at University of Toronto Press, Virgil Duff, has been nothing but dedicated and helpful throughout the publication process, patiently and promptly responding to my two thousand 'new author' questions regarding 'what happens next?' (He was also responsible,
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.251 | 0.212 |
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".