Precarious employment : understanding labour market insecurity in Canada
Bibliographic record
Abstract
Contributors include John Anderson (Canadian Council on Social Development), Pat Armstrong (York University), James Beaton (York University), Stephanie Bernstein (University of Quebec at Montreal), Jan Borowy (Ontario Public Sector Employees' Union), Cynthia Cranford (University of Toronto), Tania Das Gupta (York University), Alice de Wolff (Independent Researcher, Toronto), Andrew Jackson (Canadian Labour Congress), Andrew King (United Steel Workers of America), Kate Laxer (York University), Wayne Lewchuk (McMaster University), Katherine Lippel (University of Quebec at Montreal), Chris Schenk (Ontario Federation of Labour), Michael Polanyi (Canadian Ecumenical Justice Initiatives), Emile Tompa (McMaster University and University of Toronto), Heather Scott (University of Toronto and Institute for Work and Health [IWH]), Scott Trevithick (University of Toronto and IWH), Sudipa Bhattacharyya (IWH), Eric Tucker (York University), and Nancy Zukewich (Statistics Canada)
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".