Sex Work Governance Models: Variations in a Criminalized Context
Bibliographic record
Abstract
BACKGROUND: Under current laws, sex workers are effectively criminalized, which can lead to harmful impacts beyond arrest and prosecution for sex work-specific offenses, including eviction, search and seizure, surveillance, harassment, and deportation. Although these laws are federal, they are realized in and by policy communities at the municipal level. MATERIALS AND METHODS: Based on a qualitative and inductive study of local policy actors affected by or involved in the implementation of prostitution laws, including 65 semistructured interviews in 2014, 2015, and 2016, we identify five different governance models within a shared legal framework of criminalization. We derive these models from an exploration of interactions among actors and organizations based in selected Canadian cities, all of which are bound by federal laws that criminalize the buying of sex thus effectively criminalizing prostitution. RESULTS: Our study surfaces a diversity of traditional and non-traditional policy players who interpret and implement prostitution laws or advocate for and support sex workers. Focusing on equilibrium moments in relationships among these actors, we identify ideational frames that appear to shape dynamics among them and, in turn, give rise to different governance models. CONCLUSIONS: Our findings of different models within the same, overarching legal context are notable because it demonstrates the variability of a single law when it is implemented in local contexts. This is a contribution not just to understanding how prostitution is governed in particular contexts but also to policy and governance theory more generally. Our findings can serve in future, deductive studies that seek to determine the causes and implications of different governance models in the policy area of prostitution and beyond.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".