Bibliographic record
Abstract
My first and enduring debt of gratitude is to Kitty Smith, Annie Ned, and Angela Sidney who made me think about glaciers thirty years ago before I had ever seen one.I learned about Yukon landscapes first through their stories and then during our travels together between 1974 and 1989, as we followed out-of-the-way roads and trails in the southwest Yukon, recording place names and mapping stories associated with those places.The Council for Yukon Indians (now Council for Yukon First Nations) and the Yukon Native Languages Project (now Yukon Native Language Centre) originally encouraged and then funded our work during the late 1970s and early 1980s.I especially thank Paul Birckel and John Ritter for their confidence in and support for our work.In the Yukon, I also thank daughters of my mentors -Ida Calmegane, daughter of Angela Sidney; Stella Jim, daughter of Annie Ned; and May Hume, daughter of Kitty Smith.All have continued to share with me memories of their mothers.I am grateful to Champagne-Aishihik First Nation for including me in a visit their members made to Yakutat, Alaska, in June 1999.I am especially indebted to Marge Jackson, Diane Strand, Sarah Gaunt, Lawrence Joe, John Fingland, Steven Reid, and Sheila Greer, who have helped me to understand the local complexities of issues discussed in this book from the perspectives of Champagne-Aishihik members.Kluane First Nation invited me to attend Indian Claims Commission hearings in their community in February 2002 and enlarged my understanding of struggles their community has faced while living on the edge of a national park.I thank Mary Jane Johnson, Robin Bradasch, and Dave Joe for
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.360 | 0.231 |
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".