Bibliographic record
Abstract
In addition to the hard work of the authors, we would like to thank the following people.The supporting instructorsDr. Caroline Fox, Department of GeographyDr. Alexandrine Boudreault-Fournier, Department of AnthropologyProf. Colin J. Bennett, Department of Political ScienceDr. Brian Thom, Department of AnthropologyDr. Chris Auld, EconomicsDr. Gord Miller, School of Child and Youth CareDr. Marie Vautier, Department of FrenchDr. Allan Antliff, Department of Art History and Visual StudiesDr. Charlotte Reading, School of Public Health and Social PolicyEmily Avray (PhD cand.), Department of EnglishThe peer reviewersBehn Skovgaard AndersenBrian ColemanBryan Eric BennerCaroline WinterCarrie HillChelsea WilsonChristina SuzanneChristine TwerdoclibConstance SobseCori ThompsonEmma HughesFanie CollardeauFelipe de Lucia LoboGlenn BeauvaisHolly HoffmannJeff RapochJodi RempelJudy WalshKatie BullenKatherine BurnettKeith CherryKimberlee Graham-KnightLeslie BraggNatalia YangNatasha FosterOmolara IsiolaotanPamela SavageRamsay MalangeRobyn JoyceSarah HutchisonSusan KarimTeboho MakalimaTimothy PalmerVictoria DomonkosThe Arbutus Review teamLaurie Waye, Managing Editor of the journal and the Associate Director (Student Academic Success), Learning and Teaching CentreDan Lett, Guest Editor, Designer, and Typesetterwith support fromTeresa Dawson, Director of the Learning and Teaching CentreInba Kehoe, Scholarly Communications and Copyright Officer of the Library
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.372 | 0.228 |
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".