Bibliographic record
Abstract
Linguistic diversity is the key to Canada’s multicultural identity which it has been struggling to maintain for decades. Its language policies are rooted in two kinds of languages, the languages of European settlers and the indigenous languages spoken by aborigines who are the native residents of Canada. Despite the country’s conservation policy, social tensions and political debates abound on how it treats its languages by according them official or non-official status. Canada first developed its language policy in 1960s on account of Quebec nationalism and growing tensions between colonizing rivals. This led to establishment of policies which rendered English and French as official languages while indigenous languages got little support. Consequently, language-based discrimination is central to the nation’s social and political debates, which inform its self-image since the conquest of indigenous tribes. A combination of factors like hostile colonial policies, reserve systems and residential schools have undermined these languages and separated communities sharing common languages and traditions. This paper will assess how Canada’s indigenous tribes have fared since the implementation of national language policy which mandates protection of indigenous culture and identity. It will examine the treatment of indigenous languages in the current political milieu of Canada, and the progress made by the government towards adoption of important laws and path-breaking policies to create a future that nurtures its multicultural roots while affirming the national identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".