Discarding Sympathy, Disrupting Catharsis: The Mortification of Indigenous Flesh as Survivance-Intervention
Bibliographic record
Abstract
This essay feels its way through several incarnations of publically performed grief in the territories now known as Canada. In each, the Indigenous “actors” of these territories feature prominently, and the wounding of these individuals and their communities is addressed, presented, intervened upon, and “reviewed” in private living rooms, in public media, and on social-media sites. Through this exercise the essay attempts to discover the mechanics and affect of those “productions” that solicit and elicit a sympathetic response from their witnesses, and to weigh these against performative interventions that reject the sympathetic response and demand from their witnesses’ empathic, effective action to engender social transformation. The examination begins with the production of truth, the performance of sympathy, and the promise of reconciliation on Canada’s national stage; it then looks to public spaces of private mourning on the municipal stage. And it visits, at last, the professional stages of contemporary performance whereupon Native artists, eschewing the narratives of victimization, artfully intervene upon the spaces of Indigenous wounding, as they reconfigure our Nations’ mourning songs into anthems of Indigenous sovereignty and reveal the “sympathy” of the settler-witness to be irrelevant, unseemly, and perhaps even dangerous.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.055 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".