Bibliographic record
Abstract
As we have worked together on this book, the power of promising has revealed itself to us in ways old and new, through small acts of fidelity and grander gestures of trust.For that, we have many people and organizations to thank.We wish to thank the Alexander von Humboldt Foundation for its commitment to supporting international networks of scholarship in the humanities and interpretive social sciences.The workshop that began Making Promises and the resulting publication of this book were both made possible through Pamela Klassen's Anneliese Maier Research Award from the Humboldt Foundation, which she held from 2015-20.Focused on "Religion and Public Memory in Multicultural Societies," Pamela's Maier Award was hosted by Monique Scheer and the University of Tübingen and brought together international scholars and students at workshops in both Germany and Canada.We are grateful that the University of Tübingen also provided funds to support this publication.We also wish to thank York University for its generous support of Benjamin Berger's York Research Chair in Pluralism and Public Law, which provided significant funding for Making Promises.The Faculty of Arts and Science at the University of Toronto also provided logistical and financial support for the Religion and Public Memory Project, including for the "Making Promises" workshop.Organizationally, we are also grateful for the support and expertise of the University of Toronto Press (UTP), and its dedication to publishing rigorously reviewed, carefully copy-edited, and beautifully formatted books.We also have many people to thank and begin with the contributors to Making Promises.Ranging from senior scholars to MA and JD graduates, the authors of this book have each engaged with this project with creativity and seriousness of purpose.It has been a pleasure to work, think, and write with them.We also thank Daniel Quinlan, our editor at UTP, who has been with us from the early days of the project, attending the online workshop in 2020 and helping us to realize our vision for a
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.247 | 0.201 |
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".