“You Can’t Solve Precarity With Precarity.” The New Alberta Workers Program: An Interview With Jared Matsunaga-Turnbull, Executive Director of the Alberta Workers’ Health Centre
Bibliographic record
Abstract
In January 2013, SSEC Canada Ltd. pled guilty to three charges under Alberta’s Occupational Health and Safety Act after two of its temporary foreign workers died and two more were seriously injured on the worksite. A fine of $1,225,000—the largest ever ordered in Alberta—was paid to the Alberta Law Foundation, which administered the funds to the Alberta Workers’ Health Centre to develop and provide the “New Alberta Workers program.” In this interview, Jared Matsunaga-Turnbull reflects on the program’s peer-to-peer Occupational Health and Safety workshops for new-to-Alberta workers to illustrate how “creative sentencing” related to serious Occupational Health and Safety violation convictions can play out. He discusses what the team learned about the particular work and life context and related needs of new-to-Alberta workers that created challenges and prompted program changes throughout the three-year workshop period. Finally, Jared considers what is needed to meaningfully support new-to-Alberta workers going forward.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 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".