Fictions of Mixed Origins: Iracema, Tay John, and Racial Hybridity in Brazil and Canada
Bibliographic record
Abstract
Given the diasporic origins of the overwhelming majority of their populace, most countries in the Americas have had to indigenize themselves. Some of them have been content to simply “play Indian.” However, many others have attempted to achieve national legitimacy by fusing their Indigenous and non-Indigenous inhabitants, even if they subsequently do not always admit it. In this essay, I examine how two foundational inter-American novels, José de Alencar’s Iracema and Howard O’Hagan’s Tay John, convey the contrasting ways in which Brazil and Canada have used racial hybridity in their national imaginaries; the former by celebrating the union of the Indigenous and the European that culminates in the birth of the first Brazilian, and thus the Brazilian nation; the latter, which is set in the aftermath of the fall of Batoche and the end of the Métis national dream, by dramatizing the apparent impossibility of racial mixing in Canada. Tellingly, despite the fact one country has embraced what one might term an ideology of ethnoracial hybridity and the other has largely rejected it, both of them have been culturally and politically dominated by groups of European descent. No less important, Indigenous people seem to have become more prominent in the country that historically has been uncomfortable with ethnoracial hybridity, Canada, than with the one that ostensibly glories in it, Brazil.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".