Sex Work and Social Media: Online Advocacy Strategies
Bibliographic record
Abstract
Online communication continues to provide opportunities to connect, mobilize and disseminate information amongst direct service organizations. While the use of social media among non-profits continues to expand, there is a paucity of research that documents the extent to which online channels – particularly social media – are adopted and used amongst organizations that support sex workers. Online advocacy efforts have grown over the last decade, with sex workers and non-profit organizations at the forefront. This article evaluates the presence and social media strategies amongst organizations providing direct services for sex workers in Canada. Eighty-seven organizations operating in Canada were examined to assess both the types of social media channels used, and the online strategies employed. Results indicate there is a propensity for agencies to engage in multiple social media platforms with spaces for service users to post information in lieu of static sites that predominantly support one-way communication. Recommendations and best practices include integrating postings across platforms for efficiency, developing and maintaining safe spaces online, and focusing on channels that support multilogue communication. Keywords: Sex work, social media, knowledge
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".