Bibliographic record
Abstract
This article revisits the debate over Barker and Cox’s (2011) use of Gramsci’s distinction between traditional and organic intellectuals to contrast academic and activist modes of theorizing about social movements. Often misread as an attack on personal choices in career and writing, the distinction aimed to highlight the different purposes, audiences, and social relationships entailed by these different forms of theorizing. Discourses which take ‘scholarship’ as their starting point position ‘activist’ as a personal choice within an institutional field, and substitute this moral commitment for a political assessment of its effects. By contrast, few academics have undergone the political learning curve represented by social movements. This may explain the widespread persistence – beyond any intellectual or empirical credibility – of a faith in ‘critical scholarship’ isolated from agency, an orientation to policy makers and mainstream media as primary audiences or an unquestioned commitment to existing institutional frameworks as pathways to substantial social change. Drawing on over three decades of movement participation and two of academic work, this article explores two processes of activist training within the academy. It also explores the politics of different experiences of theoretical publishing for social movements audiences. This discussion focuses on the control of the “means of mental production” (Marx, 1965), and the politics of distribution. The conclusion explores the broader implications of these experiences for the relationship between movements and 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.013 | 0.082 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".