Bibliographic record
Abstract
In 2010 the three authors of this book came together to apply for a research grant from the Social Sciences and Humanities Research Council of Canada (SSHRC) to study "Inuit Regional Autonomy in the Provincial and Territorial North." Our initial applications were unsuccessful, but in 2012 the reviewers and evaluators finally saw things our way (or we finally listened to their advice!), and we were awarded a SSHRC Insight Grant.The result is this book.We thank SSHRC for financially supporting this project from start to finish.We are extremely grateful to the many individuals who helped us along the way.First and foremost, they include officials in Nunavik, the Inuvialuit Settlement Region, and Nunatsiavut and in the various provincial, territorial, and federal governments who generously shared their time and insights with us.Collectively, their participation in the research process was crucial in helping us to understand the paths to Inuit governance and the political structures, policy processes, and outcomes produced in these regions.In particular, we recognize Minnie Grey, Paul Bussières, and Donat Savoie for helping us to understand the intricacies and complexities of politics in Nunavik and the Canadian Arctic.We hope the findings in this book are accurate and reflective of the amazing insights that all of these people shared with our research team.We also thank our academic mentors for the support and guidance they have provided over the years.We were inspired by the work of Graham White, Frances Abele, Louis-
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.356 | 0.143 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".