Prison (im)permeability and non-visitation in Brazil: carceral violence from the standpoint of the ‘disfavoured’
Bibliographic record
Abstract
Bahia is the Brazilian state that incarcerates the smallest proportion of its population and has an incarceration rate (97.4) much lower than the national average (329.2). However, these numbers tell us little about the harms inflicted on Bahians in the name of public security. Drawing on field research carried out in/around Feminina (a women’s prison), during two periods (2010–2012 and 2016–2019), we approach carceral violence from the standpoint of ‘ pessoal da Feminina’, a category used by the imprisoned and non-imprisoned people most impacted by this prison to refer to themselves. Our analysis is oriented by the insights of Black feminist scholar-activists. Building on theorisations of the ‘sequelae’ (deadly after-effects) of gendered and anti-Black police killings, we turn attention to the injurious effects of imprisonment and the interlocking nature of carceral and police violence. Our interlocutors are Black women whose lives have been shaped by police lethality and various institutions of confinement. Taking seriously their claim that ‘ pessoal ’ are always disadvantaged, and centring their interpretations, exposes limitations of a set of increasingly common tropes in the critical prison studies literature, including narratives about carceral porosity, the provision of assistance/care and the abandonment of incarcerated women. Ultimately, we show how the relative impermeability of Feminina inflicts gendered and racialised sequelae by constraining horizontal and bi-directional exchanges of care among its ‘ pessoal ’ and producing non-visitation . We conclude that it is necessary to think critically about the concepts that structure prison research if we are to challenge rather than reinforce calls for more and ‘better’ top-down carceral assistance .
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| 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".