Redistribution and Recognition: Assessing Alternative Frameworks for Aboriginal Policy in
Bibliographic record
Abstract
In this paper, we argue that government approaches to addressing the claims of Aboriginal peoples in Canada are insufficient. Historically, these approaches have focused on redistribution. At the same time, these approaches have all but ignored recognition. We argue that a more holistic approach that addresses both redistribution and recognition is necessary. Further, we attempt to show that our approach is consistent with the tenets of liberalism. By conceiving of Aboriginal politics as such, the state may be better able to address claims. We begin by providing a theoretical overview of redistribution and recognition, respectively. Then, we proceed to show how redistribution and recognition must work together in an adequate account of justice with respect to Aboriginal peoples in Canada. Finally, we offer a conception of Aboriginal politics that fulfills this desideratum, and integrates the principle of recognition and redistribution in a way that is within the bounds of liberalism.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".