Contemporary Crises, Historical Antecedents: Refusing Vulnerability in Indigenous Speculative Fictions
Bibliographic record
Abstract
Cherie Dimaline’s (Métis) Empire of Wild (2019) and Jessica Johns’s (Cree) Bad Cree (2023) refuse to frame the violent events at their centre as isolated or incidental. Instead, authors situate crises within the long historical continuum of settler-colonialism and its impact on Indigenous communities in Canada. Catriona Mackenzie et al.’s expansive intersectional taxonomy of vulnerability defines its pathogenic variant as emerging from entrenched ‘sociopolitical oppression or injustice.’ Pathogenic vulnerability demonstrates how specific groups can experience conditions that render them more vulnerable to violence. In this article, I argue Dimaline and Johns utilise speculative tropes to interrogate widespread decontextualised state narratives of individual vulnerability. Violent events are alternatively narrated as products of their specific context – the conditions of pathogenic vulnerability conferred upon Indigenous peoples in settler-colonial nations. A central protagonist’s individual search for truth foregrounds narrative engagement with contemporary issues facing communities – Murdered and Missing Indigenous Women, Girls and Two-Spirit People (MMIWG2s) statistics, land grabs, state-sponsored industrialism and environmental and psychological devastation within post-extraction communities. Yet authors resist reasserting victim paradigms or employing a reconciliatory politics. Speculative tropes instead encourage what Jo-Ann Archibald (Stó:lō) calls storywork. Such tropes, which denaturalise violent encounters, encourage lateral thinking via nested narratives/metanarratives and embed both traditional monsters and alternative worlds, instigate storywork through inciting deeper reader engagement while foregrounding Indigenous agency, knowledge and resistance.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.066 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".