Bibliographic record
Abstract
Precarious employment entails both a heightened risk of injury and a greater likelihood of exclusion from statutory injury-compensation schemes. The contrasting cases of workers’ compensation entitlements among firefighters and farm workers in the anti-union Canadian province of Alberta provide a preliminary insight into how issue framing can be used to gain mandatory workers’ compensation coverage for workers. In addition to careful and timely critiques of legislator justifications, farm-worker advocates may be able to (a) generate shared framings among farm workers and farmers by creating a credible liability threat, (b) leverage preferential workers’ compensation access accorded to noncitizens into policy change, (c) challenge the constitutionality of the exclusion, and (d) trigger a framing process among farm workers via social media to increase pressure on legislators. These strategies offer a way to undermine the interlocking interests of farmers, politicians, and agribusiness that constrain efforts to achieve broad statutory inclusion of farm workers and achieve greater access to workers’ compensation benefits for them.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".