Sex buyers’ attitudes: A study of Toronto’s online "Escort Review Board"
Bibliographic record
Abstract
Many recognize that people who buy sexual services create a demand for services that can lead to exploitation and harms to individuals and communities involved, but little research in the Canadian context has been published to understand these buyers. The present research sought to understand the attitudes of people who buy sex towards people who provide sex services by analyzing comments posted by Toronto Escort Review Board members between April 1 and October 31, 2021. A set of 15 comments were randomly selected for each of 6 searchable characteristics: “young” (age), “spinner” (body type), “ebony” (race), “trans” (gender), “submissive” (disposition), and “greek” (activity). This research reveals an attitude of consumerism in people who buy sex that leverages online communities 1) to select almost any type of person for a sexual experience and 2) get the support of fellow members to help them pursue a desired sexual experience. However, analyzed comments also suggest that members who buy sex generally ignore, consciously or unconsciously, possible exploitation or the impact on the welfare of the individual in the short and long term. Ongoing collaborations between Canadian law makers, the criminal justice system, and health and education sectors are needed to reduce the demand in sex services. This research shows there is much more work to do to confront the consumeristic views of people who provide sex services held by people who buy sex and combat online cultures that promote the exploitation of others.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".