Bridging Police Work with the Public Health Domain: An Occupational Safety and Health Perspective
Bibliographic record
Abstract
For good reasons, public health and public policing constitute two separate constellations of public affairs governance. They widely differ with regard to their objectives, legal basis, workforce, expertise, traditions, occupational culture and many more. In conjunction to both strands of governance Occupational Safety and Health (OSH) – being both a definition and umbrella term - encompasses any kind of activity related to foster the safety and wellbeing of workers. In that regard OSH is marked by being a highly interdisciplinary, hands-on and heuristic undertaken, in particular widely acknowledged of being ‘public health-close’ and at the same time ‘security risk management-near’. That way OSH is clearly identifiable as a highly promising interface bridging police work with public health, in particular by applying mutual theory and language. This conceptual paper proposes a new perspective and view on organisational OSH, functioning well as a legitimate medium for both frontline workers but also managerial functionaries. Vice-versa organisational OSH has been identified as a suitable trigger for transferring academic stances into the rather praxis- and realpolitik-driven domain of policing. Alongside the prototypical case study of Frontex operational OSH, OSH has been proven as legitimate driver for utilising the current pandemic COVID-19 outbreak as suitable tool for breaking down existing barriers and silos between the both mentioned strands of governance. That way as additional craft and capacity OSH might enfold truly operational strength and added value.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.016 | 0.055 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".