Bibliographic record
Abstract
The three peer reviewers on the project—Daniel Gervais, Mindge Li, and Christopher May—deserve a special acknowledgment for their thorough reviews of and helpful comments on our draft chapters. So too does Hong Xue for co-hosting our Hong Kong workshop. In addition, I want to single out my doctoral student at the University of Ottawa and former research fellow with the EDGE Network, Lihong Li, not only for his organizational support but also for his substantive contributions. Turning a collection of papers into a published book was not a simple task. A debt of gratitude is owed to Andrea Kroetch and David Quayat, a current and a former student who helped me to prepare the manuscript for this book. Further thanks go to Brian Henderson, Clare Hitchens, Rob Kohlmeier, and the rest of the staff at Wilfrid Laurier University Press for their excellent editorial and promotional skills. Max Brem, Jessica Hanson, Daniel Schwanen, Stephanie Woodburn, and others at the Centre for International Governance Innovation deserve acknowledgment for important role they played in the publication of this book. As I explain in the introductory chapter, my hope is that this book project marks the beginning, not the end, of this group of authors ’ efforts to aid implementation of the WIPO Development Agenda. Each chapter in this book represents a potential spinoff for future ideas and strategic discussion. If we proceed as I hope we do, further thanks will be forthcoming in the near future.
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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 | 0.029 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".