The Brazilian Adventures of a Blue-Eyed Ojibway: An Interview with Drew Hayden Taylor
Bibliographic record
Abstract
The spread of the Indigenous storytelling gospel has no borders, as Drew Hayden Taylor has shown. The Anishinaabe writer is one of the major names in the history of Indigenous Theatre and Indigenous Literature in Canada, who has contributed to the spread of Indigenous cultures and traditions through many different genres, such as theatre, novel, short stories and films. One might wander if it is the Anishinaabe nomadic tradition or the artistic career, but the fact is that travelling is part of the nature of the “Blue-Eyed Ojibway” that lives both in the Curve Lake Nation and in Toronto, in Ontario, but is also a citizen of the world. However, the great adventurer had never travelled to South America until last year. It was our fortunate meeting in Canada in 2018 that changed the course of this story, which also resulted in an interview published in Interfaces Brasil\Canadá that year, entitled “Indigenous Storytelling in the Contemporary World: An Interview with Drew Hayden Taylor”. One more country was added to the passport of the great traveller, and one more significant bridge between Indigenous knowledges in Brazil and in Canada was promoted.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".