Safety Culture Maturity in Several Latin America Mining Activities
Bibliographic record
Abstract
Health and safety is a crucial issue in the mining industry because of the implication of fatalities in this sector. A study of safety culture maturity in several Latin America countries has been done based on the model from Filho et al. [1]. The questionnaire includes 28 items regarding the type of activity, number of employees and safety culture characteristics of the activity: Information of accidents and misses, organizational structure to deal with the information, involvement of the company in health and safety issues, the way it communicates accidents and misses and commitment of the company towards health and safety.\nThe questionnaire was completed by 58 mining company managers from Bolivia, Peru, Colombia and Mexico. Results show different behaviours depending on the type of company, cooperative or private company. When private companies are analysed, it is seen a level of maturity according to the size of the company, whereas cooperatives does not have a clear trend in terms of size apart from very small cooperatives, less than 10 employees. However, there is a remarkable difference between cooperatives that have implemented continuous improvement systems and the others. In particular, cooperatives with a continuous improvement system have been analysed, displaying much higher safety culture levels.\nTherefore, it can be concluded that private companies improve their level of safety culture as the size of the company increase, because procedures and control systems are implemented. When cooperative or small companies introduce similar systems they also achieve substantial gains, but their approach is different. Managers from cooperatives have to see economic reasons to implement it, such as the Fairmined certificate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".