Life stories and legacies in Tomson Highway's Kiss of the Fur Queen and Deborah A. Miranda's Bad Indians : a tribal memoir
Bibliographic record
Abstract
This article examines life stories and legacies of Indigenous peoples through the act of discovery, recovery, testimony, reclamation, and historical reconstruction that took place during the past century. The works of two distinct Indigenous writers will be at the centre of the present work, Tomson Highway, the well-known Cree writer, playwright, and musician; and Deborah A. Miranda, descended from the Esselen and Chumash peoples, native to the Santa Barbara / Santa Ynez / Monterey, California, area. Miranda, an established writer and poet, was professor of English at Washington and Lee University before she retired to focus on writing and creating community. The two novels to be discussed are Kiss of the Fur Queen, first published in 1998, and Bad Indians: A Tribal Memoir, published in 2013. Both novels draw heavily on the personal life experiences of their authors. The works share common themes: each offers an emotional account of life stories; each brings genocide to light and offers a unique exploration of the legacies of Indigenous genocide in northern Manitoba and California, respectively. Simultaneously, the act of discovery and historical reconstruction provides an insight into the continuous development and affirmation of the Indigenous peoples as this unfolded during the past hundred years and beyond.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.026 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".