Anu’s story: Unpacking the conflation of sex work and sex trafficking
Bibliographic record
Abstract
Using I-poems and poetic inquiry, this paper takes a case study approach to discuss the distinctions between consensual sex work and sex trafficking by situating the knowledge and lived experience of a first-generation South Asian Canadian independent indoor sex worker. Through Anu’s words describing her own experiences with both empowering work and instances of exploitation, this paper posits that engaging in the sex trade is legitimate work when workers have agency. Despite the stereotypes perpetuated in anti-trafficking discourse, especially of South Asian women, Anu defies the expected role of a helpless trafficking victim. In highlighting Anu’s story, we aim to provide a complexified and nuanced view of sex trafficking and its common conflation with consensual sex work. This conflation leads to further harm, as can be seen in Anu’s story, when anti-trafficking legal measures do not provide safety nor justice for sex workers who experience exploitation but are not perceived as adhering to controlling narratives of a “marketable victim.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".