Representation in Participatory Video: Some Considerations from Research with Métis in British Columbia
Bibliographic record
Abstract
Participatory research is now a central approach for shaping relationships between the academy and marginalized communities and people. Somewhat less developed has been the concern for ensuring that participatory processes have research products that are inclusive and accessible as well. Participatory video has emerged as a key tool in putting together process and product in ways that provide avenues for marginalized communities to participate in both forms of self-research and ways of self-representation. In this essay, the authors discuss the development and progress of two participatory video projects underway with Métis communities in British Columbia. The conditioning factors for the degree and distribution of control over self-representation within participatory video are at least as complex as those within participatory research itself, but none the less doors are opening for marginalized communities via new digital technologies.
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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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.047 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".