Bibliographic record
Abstract
Aboriginal policy and claims negotiation in Canada is seen to be a murky and perplexing world that has become an important public issue and has significant policy implications for government spending. Aboriginal land policy in Canada began as an Aboriginal initiative. In No Place for Fairness, David McNab - a long time advisor on land and treaty rights for both government and First Nations groups - looks at the Bear Island Indigenous rights case, initiated by the Teme-Augama Anishinabe, to explore why governments fail to deal effectively with Aboriginal land claims. The book, divided into two sections, includes a survey of the historical background of the Bear Island claim followed by a more personal series of reflections about what happened as the claim encountered decades of policy hurdles, court cases, public protests, and above all resistance by the Temagami First Nation. McNab provides details of how ministers and their senior officials resisted real efforts to resolve problems as well as examples of field staff resisting government attempts at resolution. He also shows that government entities such as the Indian Commission of Ontario and the Native Affairs Directorate were largely used as "mailboxes" where successive federal and provincial governments sent things they wanted to bury. No Place for Fairness is the story of what happens when Aboriginal peoples' political rights are crammed into the Euro-Canadian legal system. McNab makes a clear case that a legalistic approach to these problems is wholly inadequate and that more important things - like fairness - must be recognized as paramount if a just and lasting Aboriginal land policy is to be created.
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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 | 0.020 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.084 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".