“In search of our better selves”: Totem Transfer Narratives and Indigenous Futurities
Bibliographic record
Abstract
Much contemporary science fiction urges us to focus on eco-activism and sustainable futures in order to prevent environmental catastrophe. From a critical Indigenous and anticolonial perspective, however, the question becomes “for whom are these futures sustainable”? Set in a nondescript desert dystopia, George Miller's film Mad Max: Fury Road 2015 alludes to the westerns of yesteryear and the Australian “outback”—spaces coded as menacing in their resistance to being tamed by settler-colonial interests. This article charts how Miller's film, while preoccupied with issues pertaining to global warming and ecological collapse, replicates and reifies settler replacement narratives, or what Canadian literature scholar Margery Fee has referred to as “totem transfer” narratives (1987). In these narratives, ultimately the “natives” transfer their knowledges and then disappear from view, helping white settlers remedy the self-created ills that currently threaten their worlds and enabling them to inherit the land. In the second half, I also consider how Indigenous futurist texts offer decolonizing potentials that refute the replacement narratives that persist in settler-colonial contexts. In particular, I examine how Indigenous cultural production emphasizes the importance of the intergenerational transfer of Indigenous knowledges and refuses the hermeneutic of reconciliation that seeks to discipline Indigenous futures in the service of a settler-colonial present.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".