# Me Too: Global Progress in Tackling Continued Custodial Violence Against Women: The 10-Year Anniversary of the Bangkok Rules
Bibliographic record
Abstract
On any given day, almost 11 million people globally are deprived of their liberty. In 2020, the global female population was estimated to be 741,000, an increase of 105,000 since 2010. In order to investigate progress in the adoption of the Bangkok Rules since 2010, we conducted a legal realist assessment based on a global scoping exercise of empirical research and United Nations (UN) reporting, using detailed MESH terms across university and UN databases. We found evidences in 91 documents which directly relate to violations of the Bangkok Rules in 55 countries. By developing a realist account, we document the precarious situation of incarcerated women and continued evidence of systemic failures to protect them from custodial violence and other gender-sensitive human rights breaches worldwide. Despite prison violence constituting a complex and multifaceted phenomenon, very little research (from the United States, Canada, Brazil, Mexico, and Australia) has been conducted on custodial violence against women since 2010. Although standards of detention itself is a focus of UN universal periodic review, special procedures (violence against women) and concluding observations by the UN committees, very few explicitly mentioned women, and the implications of violence against them while incarcerated. We highlight three central aspects that hinder the full implementation of the Bangkok Rules; the past decade of a continued invisible nature of women as prisoners in the system; the continued legitimization, normalization, and trivialization of violence under the pretext of security within their daily lives; and the unawareness and disregard of international (Bangkok and others) rules.
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 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".