Bibliographic record
Abstract
In the summer of 2015, Cinematheque programmer Dave Barber and I began work on a feature documentary about the Winnipeg Film Group.With my interview background from past projects and encouragement from the Oral History Centre at the University of Winnipeg, I set out to capture extended interviews with the goal of eventually producing an oral history of the organization to accompany the documentary in production.We eventually interviewed fifty people associated with the artist-run centre, from across Canada and beyond.Each interview was transcribed and edited for clarity, with the transcript then reviewed by the interviewee.Not everyone approached was interested in participating in the documentary, or in the subsequent work of reviewing a transcript, which left some voices absent from the mosaic that follows.The challenge was ever before us, as Dave often reminded me: "In many ways, the Winnipeg Film Group is like the great Kurosawa movie Rashomon.Everyone sees their own version of the truth." 1 Our documentary would eventually be released in 2017 as Tales from the Winnipeg Film Group.Some of the interviews from the documentary are included here in edited form; one of these, with Winston Moxam, was conducted previously at the screening of his film Barbara James, at Catacomb Microcinema in 2007.Establishing Shots is a story of creative individuals, a persistent community, and a particular place.The purpose is to head back in time, to reach the
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.381 | 0.200 |
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".