Bibliographic record
Abstract
All essay collections are collaborative processes, and this book in particular has relied upon many people, groups, and institutions.We would like to begin by thanking the Languages, Literatures, and Culture Department and the Anthropology Department at McGill University, particularly the faculty and chairs whose support has been crucial to the existence of this volume.We are also tremendously grateful to the digital humanities community in Montreal, particularly Stéfan Sinclair at McGill and Michael Sinatra at the Université de Montréal, whose guidance and encouragement as senior scholars have been fundamental.As co-editors, we are so thankful for the sources of funding that have enabled us to work together for the past four years, including grants from McGill's Arts Internship Office and GREN (Groupe de recherche sur les éditions critiques en contexte numérique), and the generous support of the Fonds de recherche du Québec -Société et culture (FRQSC) and the Social Sciences and Humanities Research Council of Canada (SSHRC).This funding has allowed us to grow our expertise and research interests through collaboration on numerous projects with scholars from across the Americas and Europe, ultimately resulting in rich relationships with the contributors to this book.We are so grateful to Thea Pitman for sparking the idea for this volume and continuing to believe in its success, and to Carl Fischer at Fordham University for his eagerness in supporting our ideas and his intellectual companionship.We would like to thank Gabriella Coleman and her Bits, Bots, & Bytes digital research group for their keen editorial advice -and pizza -in the early stages of writing.To Mark Thompson, our editor at the University of Toronto Press, whose consistency and professionalism are unmatched: we have loved working
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.583 | 0.323 |
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".