Analysis of Recommendations from Mining Incident Investigative Reports: A 50-Year Review
Bibliographic record
Abstract
A systematic analysis was conducted using ten occupational health and safety commissioned reports from Canada, New Zealand, United States, United Kingdom, and Australia spanning from 1967 to 2015. The objective was to identify commonalities and differences in the key recommendations across the identified reports. The text-mining software Leximancer was utilized to analyze the content of the recommendations through the semantic extraction of dominant themes, and the relational extraction and mapping of thematic relationships against each other. The identified themes were then analyzed within the concept map to fully understand the relationships. Based on the concept map, the thematic analysis provided a longitudinal perspective of the recommendations, identifying six key themes and 49 sets of overlapping recommendations. Key themes included: health and safety hazards (n = 10), legislation, regulations and organizational structure (n = 13), emergency management and mine rescue (n = 9), training, education and competence (n = 10), technology (n = 4), and research (n = 3). The results of this analysis illustrate that the same hazards continue to be identified across reports and recommendations, regardless of time or country of origin. This indicates that the communication of recommendations and/or the strategies developed in response to the recommendations need to be further addressed.
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 | 0.037 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".