Bibliographic record
Abstract
Specific Claims is a cornerstone of the Government of Canada’ efforts to improve its relationship with Indigenous peoples. Specific Claims was introduced when the Canadian state had reduced legitimacy regarding Indigenous peoples as a result of the failed White Paper policy in 1969 and the Supreme court’s Calder decision in 1973. It promised to fulfill historical obligations towards Indigenous peoples; it has yet to meets its goals almost five decades later. Indigenous leaders are frustrated and have declared that the policy is designed to appease them while the Government of Canada continues to benefit from their lands and resources. This paper draws on qualitative coding methods to examine and explain legitimacy issues within Specific Claims. Specifically, data from parliamentary committee testimonies and three interviews indicate that Indigenous concerns with Specific Claims center around transparency, accessibility, and power. These concerns indicate problems of legitimacy in Specific Claims.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.053 | 0.030 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".