Bloody Lucky: the careless worker myth in Alberta, Canada
Bibliographic record
Abstract
As the Canadian province of Alberta has adopted neoliberal prescriptions for government, it has increasingly attributed workplace injuries to worker carelessness. Blaming workers for their injuries appears to be part of a broader strategy (which includes under-reporting injury levels and masking ineffective state enforcement with public condemnation of injurious work) to contain the potential political consequences associated with unsafe workplaces. This reflects the state's sometimes conflicting goals of maintaining the production process and the political legitimacy of the government and the capitalist social formation. This case study considers the political dynamics of occupational health and safety in Alberta to understand the escalating use of the careless worker myth over time. Alberta's emphasis on employer self-regulation has resulted in a large number of annual workplace injuries. The 2008 "Bloody Lucky" safety awareness campaign intensified this attribution of blame via gory videos aimed at young workers. This case study examines the validity of this attribution to reveal that this campaign provides workers, particularly young workers, with inaccurate information about injury causation, which may impede their ability and motivation to mitigate workplace risks.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".