Bibliographic record
Abstract
Born at a traditional Inuit camp in what is now Nunavut, Joan Scottie has spent decades protecting the Inuit hunting way of life, most famously with her long battle against the uranium mining industry. Twice, Scottie and her community of Baker Lake successfully stopped a proposed uranium mine. Working with geographer Warren Bernauer and social scientist Jack Hicks, Scottie here tells the history of her community’s decades-long fight against uranium mining. Scottie's I Will Live for Both of Us is a reflection on recent political and environmental history and a call for a future in which Inuit traditional laws and values are respected and upheld. Drawing on Scottie’s rich and storied life, together with document research by Bernauer and Hicks, their book brings the perspective of a hunter, Elder, grandmother, and community organizer to bear on important political developments and conflicts in the Canadian Arctic since the Second World War. In addition to telling the story of her community’s struggle against the uranium industry, I Will Live for Both of Us discusses gender relations in traditional Inuit camps, the emotional dimensions of colonial oppression, Inuit experiences with residential schools, the politics of gold mining, and Inuit traditional laws regarding the land and animals. A collaboration between three committed activists, I Will Live for Both of Us provides key insights into Inuit history, Indigenous politics, resource management, and the nuclear industry.
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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 | 0.001 | 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 | 0.022 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.084 | 0.031 |
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".