Bibliographic record
Abstract
The past few times that I have taught my course on religion and film I have included a number of Indigenous movies. The response from students has been entirely positive, in part because most of them have rarely encountered Indigenous cultural products of any kind, especially contemporary ones. Students also respond well to the way in which many of these films use notions of the monstrous to explore, and explode, colonial myths. Goldstone, for example, by Kamilaroi filmmaker Ivan Sen, draws on noir tropes to peel back the smiling masks of the people responsible for the mining town’s success, revealing their underlying monstrosity. Similarly, Mi’gmaq Jeff Barnaby’s debut feature Rhymes for Young Ghouls makes cinematic allusions to 1970s horror films in its depiction of the residential school system. In this paper, I will draw on these examples to discuss how examination of the monstrous in Indigenous films can help us to introduce students to the ideological power of myth, specifically in relation to colonialism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".