Decolonizing Engagement? Creating a Sense of Community through Collaborative Filmmaking
Bibliographic record
Abstract
The visual medium has the potential to be a creative avenue for enhancing awareness, critical thought and social justice. Through the prism of collaborative filmmaking, academic-activists can enrich textual analyses while creating what Jacques Rancière calls a “sense of community” among participants. This article reflects on the process of co-producing an Indigenous youth-driven documentary film, Indian Givers, which is publicly available on YouTube. It discusses the applied practice of engaging in a collaborative process with the aim of countering Western models of knowledge. The film and this article each draw into focus the experiences and stories of Indigenous youth who live in a highly polluted place commonly referred to as Canada’s “Chemical Valley.” Informed by Chantal Mouffe’s notion of agonism, I contend that collaborative filmmaking contributes to anti-oppressive and community engaged scholarship by facilitating intercultural dialogue, offering a reflexive and relational approach to research, co-creating knowledge and contributing to social action. This paper reflects on some of the challenges of collaborative filmmaking in order to contribute to academic-activist research. As an anti-oppressive research tool, collaborative filmmaking provides a forum for resistance to dominant colonial discourses while creating space for radical difference in pursuit of decolonization.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".