COVID-19 violence and the re-making of urban space through solidarity networks among transgender women in Lima, Peru
Bibliographic record
Abstract
Extreme instances of COVID-19 policy-related violence against transgender (trans) communities in Latin America highlight the imperative to better understand how gender, power, and rights are entwined within public spaces. Peru is a useful vantage point to explore the intersections between gendered power asymmetries, violence, and access to safe public spaces, given the brief COVID-19 public health policy that restricted the mobility of its citizens based on binary understandings of sex and associated cisheteronormativity. These policies restricted access to public spaces and essential services to women on Tuesdays, Thursdays, and Saturdays, and to men on Mondays, Wednesdays, and Fridays and were enforced based on sex assigned at birth reported via identity documents. Drawing on 25 in-depth interviews with Peruvian trans women, this paper examines the impacts of these COVID-19 policies on trans communities and documents grassroots activism efforts to navigate public space that ultimately demand and assert human rights both during and post-COVID-19. Findings illustrate that while short-lived, the sex-based policies and associated policing in urban public space significantly impacted the well-being of Peruvian trans women. Participants illustrated numerous community-enacted strategies to navigate COVID-19 inequities, including information sharing, crowd-sourcing funds to secure food, pay rent and/or secure housing, and grocery shopping provided by trans people for trans people. Further, community mutual aid efforts have continued and evolved years into the pandemic. Jointly, findings advance understandings of the critical role of public spaces as arenas of political struggles that reveal and challenge a wider spectrum of intersecting oppressions and power structures that trans women continually navigate and resist.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".