Remixed methodologies in community‐based film research
Bibliographic record
Abstract
This paper explores how remixed methodologies can inform research in Indigenous communities using short films, combining archival and contemporary footage. Drawing on the lineages of Indigenous and feminist community‐based research methodologies, we develop a three‐part conception of remixed methodologies. We emphasize, first, the need to resituate the process of knowledge production within relationships between researchers and Indigenous community members. Second, we stress the importance of reconsidering the intended outputs of community‐university collaboration to centre community goals. Third, we underscore how remixed methodologies can disrupt the narratives surrounding settler colonial archival resources, resituating historical footage with relation to contemporary Indigenous contexts. We apply this framework to our collaborative work with the Witsuwit'en Cultural and Language Authority and the Office of Aboriginal Education at British Columbia School District #54, combining archival and contemporary films to create Indigenous education resources. Specifically, we remixed footage of Witsuwit'en traditional activities from two 1927 National Museum of Canada films with contemporary interviews and footage of Witsuwit'en governance and land use activities. We highlight how making archival films relevant to contemporary Indigenous community goals required disrupting the conventions of scholarly authority, designing collaborative outputs to suit community aims, and resituating knowledge production within the context of Witsuwit'en resilience in the face of colonialism.
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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 | 0.068 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".