Bibliographic record
Abstract
The meaning, feasibility, and importance of scientific objectivity have been debated among public health scientists. The debate is particularly relevant to occupational health, because of frequent opposition between employer and worker interests. This article suggests that the concept of standpoint (J. Eakin) may be more useful than that of objectivity in framing discussion of work-related musculoskeletal disorders. Studies done from a "worker" standpoint can, for example, investigate and characterize environmental risk factors for work-related musculoskeletal disorders, while studies from an "employer" standpoint may concentrate on identifying individual workers likely to report work-related musculoskeletal disorders or those for whom consequences of work-related musculoskeletal disorders are more severe. Within "worker" standpoints, a distinction between "high-prestige worker" and "lower-prestige worker" standpoints can be identified in the current scientific debate about the health costs and benefits of prolonged standing vs prolonged sitting at work. Contact with workers, particularly lower-prestige workers, is critical to developing and sustaining a worker-based standpoint among researchers in occupational health. This contact can be facilitated by formal collaborations between universities and unions or other community groups.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".