Teaching Documentary Film as Research-Creation: Challenges and Possibilities
Bibliographic record
Abstract
Drawing on over ten years of experience teaching in the MFA Program in Documentary Media at Toronto Metropolitan University, I will try to address the complex reality of doing research-creation within the academy for the emerging documentary filmmakers in the program. The main premise of this paper is that documentary film and research-creation are very similar because, as a rule, research is at the heart of every documentary project: the essence of a documentary project is that it begins in critical observation and analysis and ends in story. Like academic research every documentary (student) project usually begins as an idea, a hypothesis, or a series of questions: its beginnings are in research, and the critical thinking therein. But this is not a given, or the same, for every student.
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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 | 0.051 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.056 | 0.035 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".