Health and safety in the workplace - conclusions
Bibliographic record
Abstract
The Comparative Labor Law Dossier (CLLD) in this issue 2/2015 of IUSLabor is dedicated to Health and Safety in the Workplace. Aside from Spain, we have had the collaboration of internationally renowned academics and professionals of the following countries: Belgium, France, Italy, Luxembourg, the United Kingdom, Chile, Costa Rica, Mexico, Peru, Uruguay, Canada and the United States. Without detriment to recommend our readers the reading of these articles, we have drawn the top 10 conclusions. Furthermore, we have elaborated a summary table with the most relevant issues regarding health and safety in the workplace in the different legal systems analyzed in this issue of IUSLabor. El Comparative Labor Law Dossier (CLLD) de este numero 1/2015 de IUSLabor esta dedicado a la sucesion y transmision de empresas. Ademas de Espana, hemos obtenido la participacion de academicos y profesionales de prestigio de los siguientes paises: Belgica, Francia, Italia, Luxemburgo, Reino Unido, Chile, Costa Rica, Mexico, Peru, Uruguay, Canada y Estados Unidos. Sin perjuicio de recomendar a nuestros lectores la lectura del capitulo correspondiente a cada uno de los paises citados, en las paginas que se suceden hemos incluido las 10 conclusiones principales que hemos alcanzado. Asimismo, hemos elaborado un cuadroresumen con aquellas cuestiones mas relevantes en materia de seguridad y salud laboral en los distintos ordenamientos juridicos analizados en este numero de IUSLabor. Titulo: Seguridad y salud laboral. Conclusiones IUSLabor 2/2015 Manuel Luque Parra and Anna Gines i Fabrellas 2
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".