Policy brief sobre los efectos del cambio climático en el bienestar de los trabajadores
Bibliographic record
Abstract
Climate change, evident in the global temperature shift, has multiple effects, including the increase in both physical and mental illnesses among workers. This policy brief aims to provide recommendations to promote public policies that protect mental and physical health in the workplace in the face of climate change.This policy brief followed the recommendations of the International Development Research Centre, relying on a review of indexed database documents and grey literature.Environmental factors are related to a quarter of the global disease burden. The impact ranges from food production methods to natural disasters.The green economy is a way to raise awareness of the effects on the environment and workplace well-being due to economic activities. It also promotes the development of healthy workspaces based on win-win relationships and value creation, a crucial action to mitigate the impact of climate change on organizations and protect workers' health, which in turn improves productivity
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.021 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".