Bibliographic record
Abstract
Like many first books, this one grew out of a doctoral project that I completed while doing a PhD at McGill University.It took a community to produce that earlier work.I have many individuals and institutions to thank, and it is my great pleasure to mention some of them here.I cannot thank enough my professors, peers, friends, and extended family.I had the greatest fortune of drawing on the advice of Axel van den Berg, who not only supervised my research but also guided my childlike sense of wonder when I was trying to learn everything all at once.Being an outspoken defender of McGill University Sociology Department's "Ho, ho, not so fast" approach to social change, Axel taught me how institutions never, ever change overnight.My training-through-research with Axel also taught me, among zillions of other things, how I could develop my passionate persona in writing.At McGill, I also benefited immensely from long conversations with Donald von Eschen, Kari Polanyi Levitt, and Michael Smith on economic sociology and political economy.When it came to European studies, it was Barbara Haskel who served as the pôle.I was very fortunate to carry out fieldwork at the Institut d'études européennes at the Université Libre de Bruxelles (ULB), on Barbara's initiative, as part of the EU-Canada Transatlantic Exchange Partnerships Programme.My continued interest in the political economy of the euro was only possible thanks to two institutional entrepreneurs who were extraordinarily generous to graduate students in ways more than one: Amy Verdun, who created the European Studies Program and everything to do with it at the University of Victoria, and Jane Jenson, who not only built the Université de Montreal/McGill University Institute for European Studies but was also the centre of gravity for European studies in Canada.Amy and Jane, being their resourceful selves, did everything to help my research be not only possible but also less austere at the ULB's
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.309 | 0.235 |
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".