Bibliographic record
Abstract
This collection of ten chapters and three original interviews with Québécois filmmakers focuses on the past two decades of Quebec cinema and takes an in-depth look at a (primarily) Montreal-based filmmaking industry whose increasingly diverse productions continue to resist the hegemony of Hollywood and to exist as a visible and successful hub of French-language – and ever more multilingual – cinema in North America. This volume picks up where Bill Marshall’s 2001 Quebec National Cinema ends to investigate the inherently global nature of Quebec’s film industry and cinematic output since the beginning of the new millennium. Through their analyses of contemporary films ( Une colonie , Avant les rues , Bon cop, bad cop , Les Affamés , Tom à la ferme , Uvanga , among others), directors (including Xavier Dolan, Denis Côté, Sophie Desrape, Chloé Robichaud, Denis Villeneuve, Jean-Marc Vallée, and Monia Chokri) and genres (such as the buddy comedy and the zombie film), our authors examine the growing tension between Quebec cinema as a “national cinema” and as an art form that reflects the transnationalism of today’s world, a new form of fluidity of individual experiences, and an increasing on-screen presence of Indigenous subjects, both within and outside the borders of the province. The book concludes with specially conducted interviews with filmmakers Denis Chouinard, Bachir Bensadekk, and Marie-Hélène Cousineau, who provide their views and insights on contemporary Quebec filmmaking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".