Activist Research Practice: Exploring Research and Knowledge Production for Social Action
Bibliographic record
Abstract
Research is a major aspect and fundamental component of many social struggles and movements for change. Understanding social movement networks as significant sites of knowledge production, this article situates and discusses processes and practice of activist research produced outside of academia in these milieus in the broader context of the ‘knowledge-practice’ of social movements. In dialogue with scholarly literature on activist research, it draws from the author’s work as an activist researcher, and a current study of small activist research non-governmental organizations (NGOs) with examples from movement research on transnational corporate power and resistance to capitalist globalization.. It explicates research processes arising from, and embedded in, relationships and dialogue with other activists and organizations that develop through collaboration in formal and informal networks; it contends that building relationships is central to effective activist research practice. In addition to examining how activist researchers practice, understand and validate their research, this paper also shows how this knowledge is constructed, disseminated and mobilized as a tool for effective social action/organizing.
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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 | 0.059 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.087 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".