Calling (Out) Contemporary Settlers
Bibliographic record
Abstract
In Split Tooth (2018), Tanya Tagaq (Inuit) crafts the story of a young Indigenous woman who understands and relates to other-than-human elements like land, ice, and the northern lights in ways that depart radically from the teachings of Western epistemologies. Tagaq’s acclaimed 2022 album Tongues is in many ways Split Tooth’s companion piece, borrowing lyrics from the text and using them in songs that contest the Canadian settler project. For instance, the album’s closing track, “Colonizer” attacks the Canadian residential school system while highlighting audience complicity in the projects of their settler states. In both Split Tooth and the music video for “Colonizer” (the “Video”), Tagaq opposes the Canadian settler project by foregrounding the other-than-human. In particular, the land and the northern lights function in both works to transform each into instances of what Daniel Heath Justice (Cherokee Nation) calls Indigenous wonderworks. I argue that through the common elements of land and northern lights, the Text and the Video speak with one another across borders of artistic expression to become what I call a trans-media Indigenous wonderwork, in which the Video’s pointedly decolonial music, lyrics, and images underscore and bolster the text’s more indirect decolonial message. Combined, Split Tooth and the Video act as a novel form of cultural production grounded in Indigenous ways of knowing, working together to call out settler audiences for their complicity in the settler project—past, present, and future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".