Mediated Landscapes: Technology and Environment in Recent Canadian Cinema
Bibliographic record
Abstract
A central defining feature of the contemporary era is an environmental crisis that has been triggered by the relationship of human-developed technologies to the natural world, which is leading us towards conditions of increasing collapse that will impact all future understandings of our era’s cultural production and experience. This crisis has led many to propose that the planet has entered a new geologic epoch, the “Anthropocene,” in which boundaries between humanity, nature, and technology have become blurred and no longer viable. In this dissertation, I explore this ecological crisis as a manifestation of mythological and perceptual frameworks that structure contemporary modes of experience, by examining how Canadian filmmakers are responding to such conditions through their artworks. Drawing on Marshall McLuhan’s notion of art as a “counter-environment” that, in its most potent capacity, acts as a “liaison between biology and technology” ([1973] 2003a, 207) or “Distant Early Warning system,” I explore how many recent Canadian films prioritize process, hybridity, decay, and transmutation to reveal the environmental status of media, developing new myths and metaphors that address the crisis of imagination that I argue is at the root of the environmental crisis. Through these approaches, the binaries between humanity, technology, and nature are revealed as false dualisms that have always been united within a greater ecological matrix—an insight that reflects the holistic study of media developed by the Toronto School of Communication. The environmental crisis cannot be resolved through any singular response, but rather necessitates a proliferation of new forms of critical thought, empathy, and modes of experience. Accordingly, in this study I explore a range of aesthetic approaches to interrogate forms of critical visuality in cinema that articulate the environmental invisibility of media, including narrative, documentary, and experimental works. These examples are united by a framework that seeks to cultivate perceptual sensitivities through revelatory forms over discursive approaches. By revealing the strong affinities between Canadian art and critical theory, I demonstrate how cinema can provide a fertile terrain for critiquing technological environments and their influence upon notions of embodiment, the natural world, and the shaping force of language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".