Representing Slow Violence and Resistance:On Hiding and Seeing
Bibliographic record
Abstract
Focusing on representations of violence and its resistance, this special issue engages and troubles Nixon’s conceptualization of slow violence, contributing feminist and antiracist perspectives. Nixon’s (2011) theorization of slow violence has recently seen growing interest among geographers and researchers across disciplines who are grappling with the multi-faceted nature of violence across politics, culture and history. In this issue, authors draw on the concept of slow violence to explore social and environmental injustices in the toxic sites of waste, racialized surveillance of food landscapes, caregiving, policing in gentrifying neighborhoods, and the dismantling of social housing provision. The papers gathered here document what Said (2001) called “the normalized quiet of unseen power” of making visible the invisible, while also raising critical questions of witnessing, surveillance, silencing, and refusal. What the papers have in common is an insistence that it is not only violence that is unseen, but the people themselves whose humanity is unrecognized and whose material struggles are disappeared (as made evident in “Black Lives Matter”). Collectively, we foreground the perspectives of those most affected by violence with the understanding that not only do they hold deep knowledge about their lives and experience, but that their particular vantage point is critical in understanding structural inequalities (Smith 2013; Torre, Stoudt, Manoff, & Fine 2017; Tuck & Yang 2014).
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".