Violence in Health and Work: How Criminalization Affects Forest Lawn’s Sex Workers
Bibliographic record
Abstract
Cis-and-trans women and gender diverse survival-based sex workers are a sub-population of Calgary, Alberta’s marginalized communities that are underrepresented in public health policy research. Forest Lawn is a geographically and socially isolated neighbourhood from most emergency health services in Calgary and is locally known for its higher incidence in crime, overdose deaths and poverty. This neighbourhood is the location of one of Calgary’s oldest sex work strolls (where people buy and sell sexual services in exchange for money) and to date, has never been exclusively included or examined in formal research. The focus of this thesis was to expand on previous and ongoing research with sex workers in Canada that has identified sex workers’ experiences of structural violence and feelings of being excluded from health care. Using a critical ethnographic inquiry, this study aimed to describe sex workers’ experiences in Forest Lawn and answer the following question: what do Forest Lawn’s women and gender diverse survival-based sex workers want health care providers to know about providing more compassionate care? The student researcher committed to five months of community outreach with a local sex work nonprofit and engaged five unique participants in semi-structured interviews. Thematic analysis through a critical and intersectional feminist disability lens led to the conclusions that survival-based sex workers in Forest Lawn experience structural violence, which is exacerbated by the partial criminalization of the sex work industry. It was also found that in order to challenge the pervasive culture within health care that further marginalizes survival sex workers experiences, providers should aim to incorporate trauma-informed practice into their framework of care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".