Safety education: culture, leadership and learning in the workplace: a critical discourse analysis
Bibliographic record
Abstract
A challenge of our time is that workplace fatality and injury rates have remained consistent for the past 25 years (AWCBC, 2017, Table 22). According to Association of Workers Compensation Boards of Canada statistics, since 1993 approximately 1000 workers have been killed on the job every year. The human and financial costs of these failures are high, so major incident reports are produced as means to guide how industries and corporations need to move forward on education, learning and safety as a whole. Questioning what these reports actually say is a necessity in terms of adult education for safety, but there is little in the literature that shows any focus in this area. Yet these reports are important because what they tell us, or do not, guides the future. Using critical discourse and content analysis, my study explored primarily one major accident report – The Report of the BP U.S. Refineries Independent Safety Review Panel (2007) – written in response to the 2005 BP Texas City Refinery accident. I chose this as it is very comprehensive report, available publicly, and is similar to many that have been produced. \nFindings show that on one hand, the Baker Panel actively worked to hold BP accountable for the accident, calling out the company for poor training programs and leadership staffing, indiscriminate cost-cutting and lack of investment, as well as tolerating unrealistic production pressures. In various ways, the panel named capitalism’s and profit over safety as problematic. However, the report also perpetuates a rigid, narrow view of leadership, bases its recommendations in part on the ‘myth’ of a safety culture, does not recognise workers’ knowledge, does not appear to understand or suggest anything around the importance of informal learning and mentions little about the important concept of mentorship. Further, it maintains a technical-rational status quo, supporting, even promoting, the existence of a ‘traditional’ corporate infrastructure framework that oppresses workers and inhibits their safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".