Bibliographic record
Abstract
Bill 6, the government of Alberta’s contentious farm workers’ safety legislation, sparked public debate as no other legislation has done in recent years. The Enhanced Protection for Farm and Ranch Workers Act provides a right to work safely and a compensation system for those killed or injured at work, similar to other provinces. In nine essays, contributors to Farm Workers in Western Canada place this legislation in context. They look at the origins, work conditions, and precarious lives of farm workers in terms of larger historical forces such as colonialism, land rights, and racism. They also examine how the rights and privileges of farm workers, including seasonal and temporary foreign workers, conflict with those of their employers, and reveal the barriers many face by being excluded from most statutory employment laws, sometimes in violation of the Canadian Charter of Rights and Freedoms. Contributors: Gianna Argento, Bob Barnetson, Michael J. Broadway, Jill Bucklaschuk, Delna Contractor, Darlene A. Dunlop, Brynna Hambly (Takasugi), Zane Hamm, Paul Kennett, Jennifer Koshan, C.F. Andrew Lau, J. Graham Martinelli, Shirley A. McDonald, Robin C. McIntyre, Nelson Medeiros, Kerry Preibisch, Heidi Rolfe, Patricia Tomic, Ricardo Trumper, and Kay Elizabeth Turner.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".