Bibliographic record
Abstract
The idea for this volume was hatched after hearing several excellent papers delivered at the biennial conference of the American Council for Quebec Studies in New Orleans in the fall of 2018.It was, from the start, a meeting of minds and interests that seemed as timely as it was energizing.The research presented here includes scholars at various stages in their careers, inside and outside the university setting, from France, England, and the United States.The co-editors are grateful for their hard work and expertise.Preparing this volume during a pandemic was no easy feat, and we are grateful to one another for the mutual support and encouragement that sustained us in our collaboration.It helped enormously to have a co-editor like Jack Yeager, whose knowledge, kindness, and generosity of spirit are so inspiring.Throughout, Miléna Santoro was a model of commitment, energy, and enthusiasm, and our conversations online and in person were always stimulating and uplifting.Our work was facilitated by contributors who made revisions in a timely fashion, some of them despite heavy workloads or health issues.We are also grateful to McGill-Queen's University Press and its editorial staff, in particular editor-in-chief Jonathan Crago, who stepped in at a crucial moment to move this project along.Our biggest debt of thanks must of course go to Kim Thúy Ly Thanh, whose writings and personal qualities made our project a pleasure and fuelled our motivation when circumstances seemed most discouraging.She generously granted several interviews and spontaneously
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.367 | 0.289 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".