Mixed methods approach for secondary data using survey reports from an exploration industry database
Bibliographic record
Abstract
The rich information acquired from incident and near miss reporting has been studied within high-risk industries and such industries have used statistics acquired from past incident reports to reveal trends to improve occupational health and safety. The proposed project aims to understand the specific nature of injury severity reports within Canada’s mineral exploration field to enhance existing occupational health and safety. The proposed research is unique as the data arises from the entire mineral exploration industry in Canada, gathered by Prospectors and Developers Association of Canada (PDAC), to represent a group of companies, working across Canada. Data of this magnitude, over such a long time span, in this workforce, has never been conducted before. Much research in H&S in the mining field has focused on mine workers, not on mineral exploration, due to the difference in numbers. It is difficult to extrapolate knowledge from other fields to mineral exploration, because of its high specialization, creating unique H&S challenges. Although closely linked to production mining, Mineral Exploration requires a different health and safety approach. This workforce has unique health and safety needs that arise due to: the nature of the working environment; remote locations subject to extreme weather and terrain; difficulty recruiting skilled workers in times of economic booms due to production pressures or conflicts of interest between H&S superiors and trainees; lack of available resources in the field which vary and are dependent on financial capacities of each company ; and H&S efforts heavily influenced by a company’s market capitalization meaning smaller companies often times not having one person specifically in charge of H&S or potentially be less obliged to follow or partake in H&S procedures, and in large companies it is more likely to have a whole group whose sole focus is H&S and the environment. Determining which factors influence health and safety within mineral exploration is therefore a crucial first step to better understand the safety culture, safety consciousness, and the specific needs of this field. Given that the health and safety environment of mineral exploration is multidimensional, it is pertinent that research be conducted directly within this field to bridge gaps in prevention and practice. The expected outcome for this project was twofold: i) to highlight the health and safety trends in the industry; and ii) to determine common trends, areas of importance, critical issues, and actionable training suggestions, to mitigate risk for workers. This was done by taking survey data and showcasing points for industry and occupational health and safety advocates through knowledge transfer components, to provide a deeper understanding of various components that contribute to injuries and fatalities.
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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 | 0.094 | 0.208 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".