Bibliographic record
Abstract
Social scientific perspectives on occupational safety largely characterize it as a disembodied, tangible, and easily quantifiable phenomenon. Recent research efforts have focused on exploring organizational conditions that predict occupational safety outcomes, resulting in top-down, often de-contextualized prescriptions about how to control safety in the workplace (e.g. ‘management should promote a culture of safety’). There is growing interest in how social processes of organizing, wider socio-cultural considerations, and the situated production of safety can contribute to the appreciation of the ‘lived experience’ of life and death at work. This Special Issue focuses on the socially constructed nature of occupational safety and the insight it provides in understanding broader social and organizational processes. In this article, we first describe how various social scientific disciplines share an interest in occupational safety and organizational behavior, yet rarely speak to another. We provide an overview of the five articles that comprise the Special Issue, and briefly highlight some ways forward for studying safety in organizations.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".