Indigenous Storytelling in the Contemporary World: An Interview with Drew Hayden Taylor
Bibliographic record
Abstract
Indigenous writers celebrate the resistance and survival of traditional storytelling in contemporary literature, and Ojibway writer Drew Hayden Taylor has done his part in Canada. He is an award-winning playwright who has spread the knowledge of Ojibway storytelling he gained growing up on the Curve Lake First Nation, located in Peterborough (Ontario), where he still has a home and kindly received me there. Taylor has published thirty books which include plays, novels and short stories, and is also well-known as a journalist and filmmaker, with documentaries such as Red Skins, Tricksters and Puppy Stew (2000) on Native humor, and Searching for Winnetou (2018), which opened the Asinabka Festival in Ottawa this year. One of the themes that Drew Hayden Taylor explores in his writings is identity. In a very humorous way, he uses his experience of growing up in an Indigenous community as a blond and blue-eyed Ojibway to question stereotypes associated with Indigenous people. The experience of moving from Curve Lake to the city of Toronto also gave him a critical perspective about stereotypes associated with Indigenous people and the complexities of Indigenous experience on the reserve and in city life, as we observe in his book Funny, You Don’t Look Like One: Observations of a Blue-Eyed Ojibway (1996).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| 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".