Leveraging Twitter Data and Activists’ Lived Experiences to Explore Digital Advocacy for Sex Work Decriminalization in the United States
Bibliographic record
Abstract
Contemporary community activism has been greatly influenced by the wide adoption of digital technologies, such as the use of social media. For communities whose voices are often systemically marginalized in public discourse, social media affords unique opportunities for communication to mobilize for social and policy advocacy. The digital space, meanwhile, has yet to be made equitable for all. This dissertation explores Twitter-based digital advocacy for sex work decriminalization in the United States, focusing on the opportunities in the digital space for community activism, the barriers facing activists when using Twitter for advocacy, and the resistance demonstrated by community activists in navigating barriers. Theoretically, the dissertation is guided by resource mobilization theory, discursive structure theory, the theory of connective action, and a systemic lens of digital inequity. This dissertation consists of five chapters, including an introduction, three independent empirical manuscripts, and a conclusion. Paper one adopts a sequential mixed methods design to explore how activists use Twitter connective functionalities (e.g., following a Twitter user, mentioning a user in a tweet, and replying to a user) to connect with key stakeholders and organize for sex work decriminalization. The mixed method analyses integrate findings of social network analysis of Twitter data and qualitative content analysis of interviews with activists who use Twitter for decriminalization advocacy. Paper two adopts qualitative content analyses of both tweets and qualitative interviews with activists to explore how sex worker rights activists and advocacy groups build Twitter messages to mobilize for decriminalization and how online advocacy is connected to offline advocacy. Finally, paper three adopts a qualitative content analysis of interviews with activists to explore how digital inequity shapes the barriers facing activists and how activists navigate and resist barriers. Overall, this research reveals activists’ intentional and strategic use of Twitter to organize resources, build movement alliances across political and social justice domains, and bridge online and offline mobilization for decriminalization advocacy. Meanwhile, the findings raise concerns about how intersecting forms of systemic marginalization legitimize platform-embedded exclusions, making Twitter an inequitable and risky environment for activists, especially for activists who engage in both online sex work and digital activism, to organize for advocacy. The research suggests the need for social workers and social justice researchers to critically examine and engage with the complex role of social media in community activism and the significance of conducting advocacy for digital equity with a commitment to confronting and removing structurally inequitable conditions compromising the digital participation of marginalized communities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".