Editorial Responsibilities
Bibliographic record
Abstract
Editors Jessica Lange, Editor-in-Chief, McGill University Jennifer Easter, Section Editor (Innovations in Practice), Centennial College Linda Ecclestone, Section Editor (Book Reviews), Lasalle Secondary School Éthel Gamache, Section Editor (French Language), Concordia University Corinne Gilroy, Layout Editor, Mount St. Vincent University Allana Mayer, Section Editor (Professional Development), OurDigitalWorld Rainer Schira, Layout Editor, Brandon University Ann Smith, Section Editor (Theory & Research), Acadia University Copyeditors Chris Landry, OCAD University Allana Mayer, OurDigitalWorld Emily Tyschenko, Guelph Public Library Lindsay McNiff, Dalhousie University Greg Nightingale, Western University Dahlal Mohr-Elzeki, McGill University Health Centre Libraries Andrea Quaiattini, McGill University Proofreaders Deborah Hemming, Acadia University Mylène Pinard, McGill University Tanya Ulmer, Internet Archive Canada (Alberta) Social Media & Web Design Graham Lavender, Web Design Coordinator, Michener Institute of Education at UHN Natalie Colaiacovo, Digital & Social Media Coordinator, Centennial College Translation Patrick Labelle, Translator, University of Ottawa
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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.311 | 0.354 |
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".