C OPY EDITOR President’s Message
Bibliographic record
Abstract
At this point, I’ve served about a year as President of your Faculty Association and I’m about to start my second year-long term in that post. It seems like a good time to reflect on events and issues, and perhaps even to inspire some of you to become involved. There’s nothing like a little responsibility to speed up learning. It takes about a year to see the typical things once and quite a bit of help to understand them properly. What makes FAUW/UW involvement so special is the incredible calibre and enthusiasm of the other faculty volunteers and the superb knowledge and dedication of our two staff members. It is almost an embarrassment of riches in terms of making an easy transition from knowing nothing to making a difference. With apologies to the rest, FAUW does two things mainly. One, we try to nudge policies and practices of the university in directions which improve the working conditions for faculty. Two, we help individual faculty members who find themselves in trouble with respect to terms and conditions of employment. Our ethical framework revolves primarily around three concepts: natural justice, academic freedom, and collegial governance. Every faculty member can improve UW just by becoming familiar with these three ideas. There’s a certain commonality to the university labour situations across Ontario and across Canada, so we get a huge benefit through
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.039 | 0.030 |
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".