On the Occasion of an Anniversary, Eh: Confessions of a Canadian Math Ed Editor
Bibliographic record
Abstract
As its title suggests, this commentary utilizes the 20th year of publication of the Canadian Journal of Science, Mathematics, and Technology Education as an opportunity for me, the current English language mathematics education editor, to confess. I confess to imposter syndrome and editorial naiveté. I confess to evolving from putting the pathetic in empathetic to near-total emotional desensitization. I confess to having stumbled upon the notions of form letters and desk rejections. I confess that my former French teachers would be disappointed in me. I confess to having never forgotten my first, to seeing ghosts, and to attempting to handle multiple concurrent timelines based on the geologic time scale. Lastly, most importantly, and utilizing an entirely different meaning of the word confess, I confess to giving credit where credit is due. In other words, what follows are the absolutely true confessions of a Canadian mathematics education editor.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".