Can We Interest You?
Bibliographic record
Abstract
Since it was founded in 1966, the McGill Journal of Education has been a bilingual peerreviewed, generalist academic journal open to a broad range of topics and concerns related to education. It supports the open access to information movement that is transforming the academic publishing world and the digital technology making it possible for knowledge produced by publicly funded scholars to be widely and easily available. This article reflects on its most significant changes and challenges as a bilingual generalist, open access journal with close ties to McGill, Québec, Canada, and, increasingly, the world writ large.Keywords Education; Generalist journal; McGill; Open accessRésuméDepuis sa fondation en 1966, la Revue des sciences de l’éducation de McGill est un journal académique généraliste, bilingue, évalué par les pairs et ouvert à un large éventail de sujets et de préoccupations relatifs à l’éducation. Il appuie à la fois le mouvement de libre accès à l’information qui est en train de transformer le monde de l’édition académique et les technologies numériques qui assurent une vaste diffusion etun accès facile au savoir généré par des chercheurs financés par l’État. Cet article se penche sur les changements et les défis les plus significatifs auxquels la revue a fait face en tant que publication en libre accès bilingue, généraliste et étroitement liée à l’Université McGill, au Québec, au Canada et, de plus en plus, au monde entier.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.134 | 0.085 |
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".