Toward ‘more participatory' participatory video: A thematic review of literature
Bibliographic record
Abstract
Dramatically increased accessibility to recording technologies and participatory nature of today’s information environment certainly have emancipatory potentials. Perhaps, we live in an era that Dziga Vertov once dreamed of: mass authorship of filmmaking reveals the injustice and inequality of the world. However, in reality, we are witnessing political turmoil characterized by partisan division and a surge of populism. Against such a backdrop, we need to rethink the ways to unleash the critical potential of participatory media and reenvision how it can facilitate people’s civic engagements for social transformation. By positing that very act of defining participation as a political struggle, this paper thematically reviews literature and examine how diverse forms of participation manifests through different designs of participatory video projects. Along the journey, I interrogate the premises of participatory video, explore the diversity of the approach, and identify the challenges and dilemmas for making participatory video ‘more participatory.’
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".