Bibliographic record
Abstract
Editorial Board and AcknowledgementsVolume 6, Issue 2 Editors-in-ChiefGhassan Al-Yassin and Juno Bayliss Humanities and Fine ArtsSenior Editor: Delane JustAssociate Editors: Mae McDonald, Teevin Fournier, Courtney Neufeld, Sophia Charyna, Chloe StainbrookGraduate Advisor (2019-2020): Jasmine Redford Natural SciencesSenior Editor: Sarah FoleyAssociate Editors: Gloria Yu, Rina Rast, Riley Whyte, Leland Bryshun, Bre Hipkin, Jalen Mikuliak, Brooke Coller Health SciencesSenior Editor: Nikoo SoltanAssociate Editors: Naiela Anwar, Sergey Kens, Tasker Wanlin, Anne-Sophie FortierGraduate Advisor: Valerie Rozwadowski Social SciencesSenior Editor: Jordan WellschAssociate Editors: Muhammad Awan, Robin Steeg, Brenan Smith, Courtney Hrynuik, Ziying LiGraduate Advisor (2019-2020): Bidushy Sadika LayoutLayout Editors: Rina Rast, Ziying Li, Teevin Fournier, Courtney Hrynuik, Ghassan Al-YassinCover Layout: Amy St. Jacques Communications Jordana Lalonde Staff and Faculty AdvisorsFaculty Advisor (2019-2020): Vicky Duncan, University LibraryStaff Advisor: Liv Marken, Writing Help Coordinator, Student Learning Services AcknowledgementsThe Editorial Board would like to thank the following individuals, departments and organizations for their contributions to and support of the journal: Laura Larsen, Writing Help Centre; Amy St. Jacques, Student Learning Services at the University Library; DeDe Dawson and JoAnn Murphy, University Library; Kate Langrell, University Library; Merle Massie, Undergraduate Research Initiative, Office of the Vice-President, Research; all faculty and graduate student reviewers; the College of Graduate and Postdoctoral Studies; and the University Library.
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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 | 0.021 | 0.167 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.265 | 0.290 |
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".