Representing Linguistic Minority(S) in Canada and India: Protection of Diversity Versus Rights
Bibliographic record
Abstract
Representation is the very essence of modern democracies. Any form of democracy, be it procedural or deliberative must be widely representative. Majorities as well as minorities need to be represented to voice their grievances, concerns and demands. Though representation is fundamental for both the majority and the minorities within a democratic state, care should be taken to make the minorities feel secured so that they trust the government and do not attempt any violent rebellion in the state disturbing the democratic set up. Greater representation of minority communities, help in fostering harmonious relationship among the majority and the minority and hence may lead to an effective discursive discourse within the democratic framework. Keeping this as the background the paper compares the institutions of Office of the Commissioner for Official Languages and the Office of the Commissioner for Linguistic Minorities in Canada and India respectively. Canada follows a policy of official bilingualism whereas India recognizes 22 languages as ‘national’ languages and follows a policy of multilingualism. The Canadian office is an ombudsman but the Indian counterpart is merely an investigative agency. The paper attempts to answer the critical question that whether following a policy of bilingualism and providing facilities for instructions in other languages without state support as in Canada is a better policy for protecting minority language(s) than a policy of multilingualism where the institution for linguistic minority safeguards is essentially toothless.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".