Towards an Insurrectionary Power/Knowledge: Movement-Relevance, Anti-Oppression, Prefiguration
Bibliographic record
Abstract
Despite ongoing efforts to articulate radical methods and theoretical frameworks for social movement research, the field remains embedded in exploitative, oppressive, and hierarchical modes of knowledge production. Following Foucault, I argue that this is because societies like ours, founded through racial and patriarchal violence, are invested in a regime of truth supportive of that violence. In light of this, I argue that social movements scholars need to adopt a radically different form of knowledge practice. Building on anarchist, anti-racist feminist, and anti-colonial scholarship, this paper begins by analysing how liberalism constrains social justice organizing and how academic norms foreclose accountable social movements scholarship. I then introduce three unique ethics emerging in resistance to this situation: movement-relevant, anti-oppressive, and prefigurative. The first confronts the extractive imperatives of enlightenment truth-making; the second resists its neutral and disinterested tendencies; and the third models a rejection of its hierarchical and exclusive mode of authority. I argue that together they provide scholars with a strategy for re-/orienting their research towards what Foucault theorizes as an insurrection of knowledges. These three ethical frameworks combine to facilitate an insurrectionary power/knowledge that fosters collective struggle as it progressively dismantles the regime of truth underlying social movements research.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.177 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".