Bibliographic record
Abstract
This chapter explores several cases where the constitution and/or constitutional policy specifically denigrates or stigmatises a people by defining them as 'other', as non-citizens, as unworthy of possessing or incapable of handling civic rights. European imperialism was the classic example of non-recognition applied on a global scale. It assumed the cultural superiority of the imperial overlord, and the cultural inferiority of the subject people. It would be incorrect to convey the impression that Euro-Canadians invariably look down upon Indians, but it would be equally misleading to pretend that Indians are not a stigmatised people within Canadian society. The strong constitutional affirmation of Britishness was reinforced by the assimilating doctrine of anglo-conformity outside Quebec that assumed the absorption of immigrants into the English-speaking majority in terms of both language and cultural norms. Searchers for the counter-case in Canada of demeaning, or stigmatic, or negative recognition find their example in the status Indian population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.092 | 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; both teacher heads 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".