Balancing Competing Priorities: Affirmative Action in the United States and Canada
Bibliographic record
Abstract
This Article shall present a detailed analysis of Equality Rights in the United States and Canada, and their relationship to race based government affirmative action programs as practiced in those two countries. At its most basic level, Equality Rights can be defined generally as the idea that a government must not discriminate against its citizens (i.e. treat some of them differently from others). Yet given this general definition of Equality Rights, how can one reconcile the concept with that of race based affirmative action programs? As this Article shall demonstrate, via its survey of the radically opposed American and Canadian national approaches, the promotion of Equality Rights is inconsistent with the support of formerly disenfranchised minority groups via ameliorative race based government affirmative action programs. Both national approaches, American and Canadian, swing between opposite ends of the pendulum. The American approach towards Equality Rights, in looking to the concept of equal treatment (for all citizens) as its point of departure, results in a piecemeal approach (in its affirmative action programs). On the other hand, the Canadian approach looking to the idea of the amelioration of past discrimination (at the expense of a formalistic definition of Equality Rights as constituting equal treatment under the law) as its point of departure results in a much more favorable judicially constructed framework for government affirmative action programs. Just as with the American approach however, there is a tradeoff involved, as the Canadian approach results in an environment much less inclined to take seriously dissenting arguments that such programs result in an environment where the state, in looking to assist formerly disenfranchised minorities, ends up doing so at the cost of treating its citizens unequally.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.015 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".