Bibliographic record
Abstract
A behind-the-scenes account of a cultural institution that made a distinctive mark on Canadian film Establishing Shots captures a diverse group of filmmakers in an immersive oral history of one of the most important and notorious artist-run centres in Canada: the Winnipeg Film Group. Both a deep dive into the life of an internationally renowned institution and an exploration of the growth of an experimental film movement, this richly illustrated collection of interviews produces a vibrant picture of the Winnipeg Film Group’s origins, successes, failures, and ongoing impact. Formed in 1974 as a membership-based film production, training, and exhibition cooperative, the Winnipeg Film Group was part of a wave of artist-run centres funded by the Canada Council for the Arts. Kevin Nikkel’s candid conversations with twenty-nine administrators and filmmakers— including Guy Maddin, Shawna Dempsey, and Matthew Rankin—reveal the precarious path of independent artists, struggles for equality within the industry, and the importance of place in their work. An engaging resource for scholars and historians of Manitoban and Canadian culture and film, Establishing Shots also shows emerging filmmakers how other artists got their start and learned their craft.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".