Critiquing the politics of participatory video and the dangerous romance of liberalism
Bibliographic record
Abstract
In this article I argue that participatory video must acknowledge its often technocratic, liberal presumptions, and take a more critical look at the political underpinnings of ‘empowerment’ and ‘voice’. I am interested in how we can use participatory video while resisting the romance of community, seeing beyond short‐term individualist approaches towards a longer‐term collective project of social justice. A reflexive approach to how power and agency work within participatory video is essential if the method is going to effect change and not merely manage social conflict. While the participatory video process can be discussed from many perspectives, I focus here on a critique of the often‐hidden politics of participatory video, its relation to academic research and in turn, to project participants within a progressive social change agenda.
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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 | 0.114 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.130 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.014 |
| 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".