Alternative Narrative Forms, Exposure, and the Limits of Formalized Truth-Telling: Giving Accounts Though New Methods in Indigenous Art
Bibliographic record
Abstract
This paper examines how individual truths concerning the atrocities and ruptures in Indigenous history, and ongoing cultural continuity in Indigenous society (despite these occurrences) can be located in current movements in Indigenous artwork. It draws upon both Judith Butler’s work on giving an account of oneself and Foucault’s notion of parrhesia to provide a frame for this engagement, and to argue for innovations in Indigenous art as indicative of methods of giving personal accounts and truth-telling that exceed the containable narratives of formal documentation. Through examining new interventions by Indigenous artists—the performance art work of Anishinaabe Canadian artist Rebecca Belmore, and the multimedia work of Kevin Lee Burton, who is Swampy-Cree—it identifies their works as exemplary of how Indigenous artistic interventions continue to formulate new methods of speaking truth to power grounded in cultural-specific forms of narrating personal truths by incorporating a variety of media and emphasize interactivity in their work. This paper ultimately argues that in the creation of art that shares personal truths and give these accounts while also acknowledging narrative absences and gaps, these artists convey the possibility of Indigenous art to share truths that might not otherwise be acknowledged by official historical record.
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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 | 0.031 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.090 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".