Indigenous Ecocinema: Decolonizing Media Environments
Bibliographic record
Abstract
Introducing the concepts of d-ecocinema and d-ecocinema criticism, Monani expands the purview of ecocinema studies and not only brings attention to a thriving Indigenous cinema archive but also argues for a methodological approach that ushers Indigenous intellectual voices front and center in how we theorize this archive. Its case-study focus on Canada, particularly the work emanating from the imagineNATIVE Film + Media Arts Festival in Toronto--a nationally and internationally recognized hub in Indigenous cinema networks--provides insights into pan-Indigenous and Nation-specific contexts of Indigenous ecocinema. This absorbing text is the first book-length exploration foregrounding the environmental dimensions of cinema made by Indigenous peoples, including a particlarly fascinating discussion on how Indigenous cinema’s ecological entanglements are a crucial and complementary aspect of its agenda of decolonialism. Additionally, see West Virginia University Press Booktimist's Q&A with author Salma Monani: https://booktimist.com/2024/12/12/the-author-of-indigenous-ecocinema-describes-new-ways-to-approach-indigenous-responses-to-climate-issues/
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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 | 0.004 | 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 | 0.009 | 0.047 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".