Occupational Health and Safety(OHS) in Small and Medium Size Enterprises (SMEs): A Primary Review1 SANTE ET SECURITE
Bibliographic record
Abstract
Problems of occupational health and safety(OHS) in small and medium size enterprises(SMEs) in that mainly are private enterprises, are severe in China where as, the corresponding theoretical study are lagged behind regretfully. This paper summarizes the representative progress in this discipline simply. The exploratory results will be used to make an initial evaluation of SMEs needs, and will help orient future research. Key words: Occupational health and safety, Small and Medium Size Enterprises, review Resume: Les problemes de Sante et securite professionnelles(OHS) dans les petites et moyennes entreprises(PME) sont principalement ceux dans les entreprises privees et sont graves partout en Chine. La recherche theorique correspondante est largement et malheureusement arrieree. Cette these fait un resume sur le progres representatif dans cette seule discipline. Les resultats exploratoires seront utilises pour faire une evaluation initiale des demandes des PMEs et nous aideront l’orientation des recherches futures. Mots-cles: Sante et securite professionnelles, les petites et moyennes entreprises, revue
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| 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) | 0.003 | 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".