“Catherine Tekakwitha, who are you?” — The Indigenous Female Body in the Colonial and Post-Colonial
Bibliographic record
Abstract
In 2012, the Mohawk saint Catherine Tekakwitha was finally canonized by the Catholic church. She has been the subject of many accounts and narratives —both historical and fictional—and figures as the main subject of Leonard Cohen’s 1966 novel Beautiful Losers. While having been lauded for its post-modernist and presumably postcolonialism stance on Tekakwitha’s figure, Cohen’s novel remains controversial in its depiction and appropriation of Indigenous womanhood. Beautiful Losers relies heavily on missionaries’ accounts of Tekakwitha and is entrenched in the male protagonist’s sexual claim and fixation on her character. Given the significant status of women in Indigenous communities, I argue that Cohen’s novel not only participates in an ongoing violation of the Indigenous female body but also denies the integrity of Indigenous family structures and their social as well as narrative authority. It hinders, rather than encourages, a shift in narrative authority pertaining to Canada’s colonial heritage. While Cohen’s text remains a necessary testament to the shortcomings and failures of history and its criticism, what is required in forthcoming scholarship and narratives dealing with Tekakwitha and figures similar to her is a narration originating in Indigenous communities. An emergence of such narratives requires a definite reckoning with Canada’s violent history of mistreating Indigenous womanhood that continues to this day.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".