Bibliographic record
Abstract
This chapter focuses on the porous and shifting borders of contemporary Quebec filmmaking through the example of six recent films, several of which are directed by women, immigrant, and Indigenous filmmakers, and many of which feature a diverse cast of characters who speak a number of languages besides French. These are Uvanga (Marie-Hélène Cousineau and Madeline Ivalu, 2014), Une colonie ( A Colony , Geneviève Dulude-De Celles, 2019), Montréal la blanche (Bachir Bensaddek, 2016), Le meilleur pays du monde ( The Greatest Country in the World, Ky Nam Le Duc, 2020), Pays ( Boundaries, Chloé Robichaud, 2016), and Guibord s’en va-t-en guerre ( My Internship in Canada, Philippe Falardeau, 2014). The author examines the ways that these films navigate the conceptions and realities of hard and soft borders and borderlands, and ultimately argues that Quebec cinema itself functions as a borderland, a space of translation and exchange that reevaluates positions between reimagined local and reoriented global frameworks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.087 | 0.015 |
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".