Bibliographic record
Abstract
First and foremost, my thanks is due to Anahareo's family, including her daughters, Katherine Swartile and Anne Gaskell, and her grandchildren, Glaze and Sandra McKay, for their help and enthusiasm in bringing Devil in Deerskins back into print.Katherine in particular has been immensely generous in telling me stories about her mother, initially visiting me in Vancouver, and then inviting me to her home to share her photographs, letters, interviews, and clippings.It was she who conveyed to me most vividly what an extraordinary woman her mother was: a Mohawk woman ahead of her time, fiercely committed to a life lived for the animals and for the land.Although I never had the chance to meet her, I would also like to acknowledge Anahareo's first daughter, the late Shirley Dawn Richardson, who played a pivotal role in helping Anahareo revise and prepare Devil in Deerskins for publication in 1972.Along with the rest of our editorial collective, I am deeply indebted to Warren Cariou, who conceived of the series, First People, First Texts, and who, in 2010, first brought together scholars and writers to discuss which of the many Indigenous texts now out of print most needed to be rediscovered by a new generation of readers.I am very thankful for all that I have learned while listening to each member of the collective speak during the impassioned discussions hosted by Warren in the cold of
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.277 | 0.177 |
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".