Bibliographic record
Abstract
The Canadian Charter of Rights and Freedoms contains not one but two kinds of equality rights—general equality rights, set out in section 15, and linguistic equality rights, set out in sections 16 to 23—but the relationship between them is not well understood. Do official language rights rest on a distinct set of values, or do they simply instantiate the same general principle expressed in section 15? If the former, what are these values, and how do they relate to other principles of constitutional justice? The matter is further complicated by the need to account for the special constitutional status of Indigenous peoples, who also claim a form of equality. If we are to do justice to all concerned, we need to determine if and how these different claims to equality can and should fit together. However, this requires that we have a clear account of their underlying principles, and our understanding of linguistic equality in this respect lags far behind. While the concept of general equality and the status of Indigenous peoples have both received sustained theoretical attention, linguistic equality has not, leaving a number of fundamental questions—namely its basic analytical structure and its moral foundations—unresolved. The purpose of this article is to lay the groundwork for a theoretical account of linguistic equality, one that situates this concept within a broader framework of constitutional values that includes general equality rights and the rights of Indigenous peoples.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".