“Your behavior is not welcome here…”: forced internal displacement of sexual and gender minorities in Kenya
Bibliographic record
Abstract
Background: While migration studies have boomed, little is mentioned about internal displacement of queer persons. In Kenya, internal displacement of sexual and gender minorities is often overlooked and not well understood, which results in increased marginalization and vulnerabilities. The article provides an in-depth analysis of forced serial internal displacement trajectories of sexual and gender minorities, and its effect on navigating socialites and livelihoods. Methods: We draw on qualitative data conducted between 2010 and 2023 using participant observations, in-depth interviews, case histories, and focus group discussions with LGBTQ+ persons, as well as mothers and fathers of gay men in Kenya. We used a thematic approach and principles of interpretive anthropology to organize and describe the meaning of the data as a continuous process. Broader themes were identified from the beginning of the first study, resulting in the development of a codebook framework that was reiterated over time. Findings: Our findings show that sexual and gender minorities are forced into internal migration. Factors informing are located at the individual, interpersonal, institutional, and communal levels. We identified three main themes with eight subthemes. The main themes were (1) reasons for migration, which were often related to stigma, violence and oppression, (2) patterns of migration, which primarily had rural to urban patterns and instability, and (3) kinship, networks, and social systems, which required rebuilding, and gaining "respectability" from kin. Conclusion: There can be little doubt that sexual and gender minorities face stigma and discrimination across levels of the socioecological model, and that in most cases, there has been multiplied grievances and anti- LGBTQ+ activities instigating serial forced migration. Forced serial displacement of sexual and gender minorities in Kenya requires research attention, and it might be better served if examined through the lens of "forced migration," given the non-voluntary aspects of fleeing and displacement. Rethinking LGBTQ+ IDPs through the lens of coercion may better capture the lived experiences given the structural stigma and violence in which they live and cannot escape.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".