RESEARCH Open Access The legislative background
Bibliographic record
Abstract
is available at the end of the articlesafety is at risk because of their work comprise a quarter in Finland (close to the European average), nearly one third in Lithuania and almost a half in Latvia. Similar figures illustrate the proportion of working people who think that their work mainly affects their health nega-tively. However, work can also affect the health of work-ing people in a positive way. EWCS revealed that 28.7 percent of Finnish employees think that their work are around the European average (7.3 percent). Thus employees ’ opinions on working conditions and their ef-fect on health seem to be better in Finland than in the two Baltic countries. Together with other occupational health services, workplace health promotion (WHP) can contribute sig-nificantly to the health of the working population. The Luxembourg Declaration on Workplace Health Promotion in the European Union [2] defines WHP as “the com-bined efforts of employers, employees and society to im-prove the health and well-being of people at work”. * Correspondence:
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.588 | 0.369 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".