Challenging Convictions: Indigenous and Black Race-Radical Feminists Theorizing the Carceral State and Abolitionist Praxis in the United States and Canada
Bibliographic record
Abstract
Abstract This essay, with accompanying lesson plan, explores how race-radical women of color feminist activists—in particular, Black and indigenous feminists—identify, conceptualize, theorize, and resist the carceral state violence of white settler societies in both Canada and the United States. This critical ethnic studies intervention focuses on the theoretical interventions driven by indigenous and Black race-radical feminists and how this has placed these activists at the forefront of anti-violence movement-building. Such an intervention specifically upholds the tensions within and refuses to collapse the radical and revolutionary political traditions and approaches of Indigenous movements for sovereignty and Black race-radical liberatory traditions. This transnational, comparative focus helps us to not only identify and understand but to create multiple strategies that dismantle the carceral state and the racialized gendered violence that it mobilizes and sustains. This essay asks the following questions which move beyond introspection or interrogation of texts about violence into compelling conversations that highlight the interlocking nature of interpersonal, sexual, and carceral state violence: How have indigenous and race-radical feminists identified and theorized the legitimized violence of the carceral state? What questions have those diverse identifications and theoretical understandings led activist scholars currently theorizing the carceral state to ask? And what insights have those critiques generated in the activist scholarship on social movements dedicated to anti-racist, feminist anti-violence, Indigenous decolonial, and anti-prison abolitionist praxis? Proceeding from the argument that both prison abolitionist praxis and race-radical feminist praxis are inherently and primarily pedagogical, the accompanying lesson plan attempts to explore the multiple ways Indigenous and race-radical women of color feminists learn, teach, and organize about carceral logics and prison abolition inside and outside the classroom in a manner that teaches against the grain of carceral common sense.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.075 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".