Under lying conditions of gender‐based violence—Decolonial feminism meets epistemic ignorance: Critical transnational conversations
Bibliographic record
Abstract
Abstract This article engages a doubled conversation, between South Africa and the United States, about gender‐based violence and the curious epistemic silence, even in critical psychology, about gendered and racialized violence as a deep sedimentary, transnational and transhistoric, layer of (in)human(e) existence. In this article, we lift up the long history of underlying conditions of state‐sponsored and socially enacted violence, and we also problematize how social science scholarship has been designed, underlying conditions, such that anti‐Black violence and gender‐based violence are routinely (mis)represented as if idiosyncratic ruptures—microaggressions or battered women—in an otherwise smooth social fabric. Through the lens of decolonial feminism, we examine how the COVID‐19 crisis makes public the gendering of violence, especially against Black women, as if it were a spike, obscuring how pervasive and enduring it is—a constant moan in South Africa, India, the United States, among native women in Canada, and other places around the globe. We end by calling for critical scholarship that peels back the symptom of gender‐based violence, recognizes the history and ongoing structural enactment of racialized and gendered violence, and excavates the bold and relentless heartbeat of resistance narrated in quiet and loud demands for dignity, liberation, and desire.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.091 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.012 |
| 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".