Framing and blaming: construction of workplace injuries by legislators in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Legislators in the Canadian province of Alberta have successfully resisted pressure to increase state injury-prevention efforts. OBJECTIVES: This study seeks to identify the narratives used by legislators to manage political pressure for increased injury-prevention efforts. METHODS: Narrative analysis of legislative transcripts from 2000 to 2012. RESULTS: Three narratives are identified in the data: (1) injuries are caused by ignorance and inattention, (2) workplaces are safe and getting safer, and (3) risk is inevitable and mitigation is (too) expensive. Each narrative has 2-4 subcomponents. CONCLUSIONS: The consistency of the messages delivered by legislators over time suggests an intentional effort to frame workplace injury in ways that manage political pressure for greater state efforts to prevent workplace injuries while maintaining the government's legitimacy. The narratives used by legislators draw on widely held beliefs about workplace injuries, including the careless worker myth and the notion that safety pays.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".