Criminalization of occupation: Articulating a legal perspective within occupational science
Bibliographic record
Abstract
Occupational science literature increasingly addresses occupations classified as illegal. We examine legal sanctioning, globally and locally, of two occupations: i) search and rescue activities to aid asylum seekers and ii) sex work. We draw on comparative legal research and critical historical legal methodologies to explore how occupations become framed as criminal in certain places and certain points in time, but not in others. Relying on comparative case studies of Italy and Poland, this paper examines the criminalization of search and rescue activities in aid of asylum seekers and highlights how two States (countries) rely on criminal law to deter the entry of some individuals while welcoming others. The analysis of sex work demonstrates that legislation regulating women’s bodies reinforced racial hierarchies throughout the British colonies. This analysis focuses on historical and contemporary Canadian legislation which continues to marginalize sex workers to this day. We illustrate that societal power relations manifest through criminal law, with individuals and groups disproportionately disadvantaged and marginalized in relation to factors such as poverty, gender, mental health, oppression, colonization, and classist social systems. Criminalization of occupations can be understood to compound marginalization, human rights violations, and occupational injustices experienced by some individuals and communities. A critical understanding of legal systems and their impact on lived experiences is beneficial to occupational scientists who aim to explore occupations that are, or have been, categorised as illegal.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.024 | 0.167 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".