Bibliographic record
Abstract
A book like this is truly a cooperative effort.We had considerable support throughout the creative process, but as editors we take responsibility for any errors or omissions.Our greatest gratitude goes to the thirty-one authors who contributed chapters to the book.They are all highly experienced experts in their fields and people of action who have learned to express themselves in as few words as possible, but we asked them to do something that was outside their comfort zone: to step back and take a long, detached view of how they did their jobs and to provide a detailed discussion of how they operated.Several of them initially demurred on the grounds that they were not capable of shifting gears in this way.Ultimately, we found them too modest.In fact, we are very pleased and indebted to them for the thoughtful analytical chapters they have produced.In the course of developing this book, we had the wonderful experience of working with Linda Rapp, who, despite illness, crafted the distinctive chapter now titled "Nurturing the Community's Soul Source."We were all saddened by Linda's untimely passing.We believe that her imaginative and community-focused chapter is part of her legacy as a great city manager.We were given valuable encouragement along the way.Zack Spicer, then of the Institute of Public Administration of Canada, encouraged our efforts and suggested that we approach the University of Toronto Press.Daniel Quinlan of
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.250 | 0.183 |
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".