Addressing Precarity in the Profession
Bibliographic record
Abstract
A new ad hoc working group has emerged from Council\ndiscussions about employment prospects for historians. This\nWorking Group on Precariously Employed and Non-Tenured\nTrack Historians will be coordinated with, and\nintegrated into, the “outreach” portfolio of the CHA. The\nworking group reflects both continuing trends and changing\neconomic realities for historians. In the former case, many\nhistorians have long been employed in a range of jobs in\nresearch, government, heritage, NGOs, and more. We recognize\nthat those areas of employment may become more\nand more important to history PhDs as changes in post-secondary\neducation have led to fewer full-time, permanent\npositions, a veritable shrinking of the university professoriate.\nThis fact of life seems unassailable. As data taken from\nthe Council of Ontario Universities, recently published on\nthe CHA indicate, the percentage of teaching done by those\nwith tenure-track jobs is barely a majority: “55% of courses\nand student enrolments are taught by full-time faculty members,\n….At the undergraduate level…part-time instructors…\nteach 46% of students and 50% of courses.” The increasing use\nof precarious labour in the university sector is only one factor\nreshaping the profession; another is the sad reality that many\nhistory departments are facing shrinkages as universities’ put\nfewer resources into the Humanities.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".