Occupational security: A holistic values-based framework for supporting occupations and safety
Bibliographic record
Abstract
In an ongoing era of global-scale, cascading, and intersecting crises, including climate change impacts, ongoing armed conflicts, and the COVID-19 pandemic, security is becoming increasingly challenging.The escalating dangers and restrictions arising from these events, adversely affecting life itself and related occupations, has resulted in insecurity disproportionately affecting the most vulnerable and marginalized communities.This often contributes to outcomes that directly relate to security, such as poverty and homelessness.In this conceptual work, we present occupational security as a holistic means to understand and address safety concerns for individuals and populations adversely affected by crises, that have both local and global impacts.Besides global crises, long-term, ongoing, and interrelated security risks from racism, poverty, lack of shelter, and colonisation are also in the purview of occupational security.This concept extends the notion of occupation by including non-humans.Using a values-based framework, occupational security challenges neoliberalism and neoliberal notions of security, which are individualist in nature; and instead, provides a collectivist approach.The values proposed for this framework incorporate sustainability, justice, peace, compassion, authenticity, and accuracy to ensure occupational security and safety for all.We propose that the framework of occupational security has the potential to enhance occupational science scholarship, as well as facilitate collaborative engagement with wider disciplines concerned with issues of security.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".