Violence and Victimization in Interactions Between Male Sex Workers and Male Clients in Mombasa, Kenya
Bibliographic record
Abstract
Male sex workers (MSWs) and male clients (MCMs) who engage their services face increased vulnerability to violence in Kenya, where same-sex practices and sex work are criminalized. However, little is known about how violence might arise in negotiations between MSWs and MCMs. This study explored the types of victimization experienced by MSWs and MCMs, the contexts in which these experiences occurred, and the responses to violence among these groups. We conducted in-depth interviews with 25 MSWs and 11 MCMs recruited at bars and clubs identified by peer sex worker educators as "hotspots" for sex work in Mombasa, Kenya. Violence against MSWs frequently included physical or sexual assault and theft, whereas MCMs' experiences of victimization usually involved theft, extortion, or other forms of economic violence. Explicitly negotiating the price for the sexual exchange before having sex helped avoid conflict and violence. For many participants, guesthouses that were tolerant of same-sex encounters were perceived as safer places for engaging in sex work. MSWs and MCMs rarely reported incidents of violence to the police due to fear of discrimination and arrests by law enforcement agents. Some MSWs fought back against violence enacted by clients or tapped into peer networks to obtain information about potentially violent clients as a strategy for averting conflicts and violence. Our study contributes to the limited literature examining the perspectives of MSWs and MCMs with respect to violence and victimization, showing that both groups are vulnerable to violence and in need of interventions to mitigate violence and protect their health. Future interventions should consider including existing peer networks of MSWs in efforts to prevent violence in the context of sex work. Moreover, decriminalizing same-sex practices and sex work in Kenya may inhibit violence against MSWs and MCMs and provide individuals with safer spaces for engaging in sex work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".