Bibliographic record
Abstract
Abstract: This article demonstrates the indissociable role of empathy in sustaining the systemic violence of extractivism, a term describing the global drive to exhaustively extract resources. The article contends that empathy, as depicted in Marian Engel's Bear (1976), not only fails to serve as a corrective for settler colonial guilt but also reinforces extractivist logic. Although Engel has not been widely recognized as one of the Canadian authors addressing colonial dispossession and ecological depletion, her novel offers a witty exploration of the Canadian natural world and Indigeneity through the way that Lou, the protagonist of the novel, practices empathy. After Lou attempts to form a romantic relationship with the eponymous bear, the animal strikes her on her back, leaving a painful wound that is often interpreted as a symbol of her repentance and personal growth. However, its significance in the context of racialized empathy becomes more pronounced when compared to the strikingly similar slashed upper torso of an Indigenous woman in Anishinaabe artist Rebecca Belmore's photograph Fringe (2007). Engel's novel suggests that while empathy toward Indigenous people and animals can catalyze ethical action, it can also re-enact an extractivist ideology. In Lou's case, empathy causes her both to appropriate Indigeneity while exploiting access to resources unavailable to Indigenous people and to instrumentalize her own sexuality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.022 | 0.032 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".