Bibliographic record
Abstract
Considerable time and energy has gone into producing this volume and there have been many people along the way who provided support and assistance to the authors.The editors would like to thank everyone who contributed to this project.To begin with, we would like to express our gratitude to the contributors to this volume.This project began with a workshop held at the University of Ottawa in May 2012.This event itself was a collegial occasion that led to the production of this volume, but equally notable is the fact that the workshop led to an enduring network of Canadian scholars from a wide range of perspectives dedicated to the study of social movements and activism.An edited volume is by nature a collaborative project and can only work when contributors are as committed to the outcomes as the editors; as we made repeated requests for revisions throughout the process, our contributors demonstrated this commitment by consistently responding with timely professionalism.And while they do not have papers in this volume we are also grateful for the participation and contributions of Karen Stanbridge and Rima Wilkes.We would also like to thank Nathan Young for his generous
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.289 | 0.192 |
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".