Bibliographic record
Abstract
Tis project followed the process commonly used at the Centre for Studies in Religion and Society (CSRS) at the University of Victoria when putting together an edited volume: authors meet twice, engage seriously with colleagues' draft chapters, and submit fnal versions that evidence a signifcant amount of interaction with other contributors' ideas.Tis process can be a difcult one, but it ideally results in a volume that readers experience not as a collection of disparate essays but as the product of an organic conversation among peers who approach an important intellectual phenomenon in a coherent manner.We would like to thank our authors -from both sides of the national border and from several disciplines -for their willingness to engage in this long but rewarding process.We would also like to thank Rachel Brown and Noriko Prezeau of the CSRS.Both provided invaluable assistance in keeping our project on track, our authors in communication, and our drafts in order.Chelsea Horton served as the project's research coordinator as well as a close collaborator on interviews, feldwork, survey research, data management, and ethics protocols.She and Paul Bramadat travelled quite a long road together -both literally and fguratively -for this project, and she never ceased to impress with her good humour, professionalism, sound judgement, and intellectual acumen.Her organization of the data made it easily accessible to our authors.Tanks, also, to James Wellman of the University of Washington for hosting our second team meeting in Seattle in 2019.We would also like to express our gratitude to the Survey Research Centre at the University of Waterloo (https://uwaterloo.ca/survey-research-centre/)for its key role in the Pacifc Northwest Social Survey (PNSS) data collection and cleaning in 2017.More broadly, we ofer a big thank you to all of the research teams that made their survey data available and open access over the yearssurvey data that we used in this book to complement our own.Tey include
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.328 | 0.183 |
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".