A Critical Appraisal of the Legal Framework for the Promotion of Workplace Safety in Nigeria
Bibliographic record
Abstract
Abstract Over 2.8 million workers die annually from workplace injuries and diseases, while an additional 160 million suffer from non-fatal work-related injuries and diseases. Many countries have enacted occupational safety and health (OSH) legislation, in line with the requirements of the International Labour Organization. Using a comparative legal research methodology, this paper examines the legal framework on workplace safety in Nigeria. It argues that the legal framework is fragmented and contains obsolete regulations that fail to cover all categories of workers. Therefore, it recommends reform to merge the various occupational safety and health laws currently in force in Nigeria. Furthermore, it recommends sensitivity to changes in the workplace arising from advances in technology and work formats. The study contributes to the theoretical discourse on OSH by developing a contextualized framework for analysing OSH frameworks in developing economies such as Nigeria.
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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 | 0.047 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".