Comparative Analysis of Methodologies for Occupational Safety Risk Assessment in an Artisanal Woodworking Industry
Bibliographic record
Abstract
The World Health Organisation (WHO) and the International Labour Organisation (ILO) estimate that 81% of deaths are related to occupational accidents.The management of occupational safety risks is substantial in companies, allowing the identification and evaluation of accidents caused by the lack of compliance with protocols and regulations in work activities.The objective of this study is to evaluate occupational safety risks in a woodworking store in the parish of Atahualpa-Ecuador, by comparing three methodologies (William T. Fine, Colombian Technical Guide (GTC-45, an acronym in Spanish) and Hazard Identification, Risk Assessment and Control Measures (IPERC, an acronym in Spanish) for the proposal of guidelines for the prevention of occupational risks.The methodology focuses on three phases: (i) selection and description of the case study, (ii) comparative analysis of occupational risk assessment methodologies, and (iii) proposal of strategic guidelines using Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis.The occupational risk assessment shows that the William T. Fine methodology was 75% effective due to its adaptability to other industries and contribution to a safer working environment.GTC-45 followed this with 65% effectiveness and IPERC with 50%.Finally, this assessment ensures operational stability to minimize occupational risks in the short term.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".