Editorial: Mini-special issue on Bill 23
Bibliographic record
Abstract
This issue is unique in more than one way.It is the second of our general issues containing a mini-special issue.And this mini-special issue is truly distinctive, unprecedented even in journal annals, in being the result of a shared concern and simultaneous call across three education journals in Quebec: Revue des sciences en l'éducation, Formation et profession, and the McGill Journal of Education/Revue des sciences de l'éducation de McGill (MJE/RSEM).The call invited authors to reflect on the implications, locally and internationally, of Quebec's Bill 23 (now law), with its greater concentration of powers in the Minister of Education and its accelerated streamlining of teacher preparation.The lead special editors on this minispecial issue at the MJE/RSEM were Simon Collin (UQAM), Geneviève Sirois (Université TELUQ), and Paul Zanazanian (McGill).In their mini special issue editorial, they further explain the issue's focus as well as introduce the three featured articles.We turn first to the special issue editorial followed by the general issue editorial.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.076 | 0.044 |
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".