Policy Speaks Volumes: How Canada's Bilingual Status Affects Indigenous Languages
Bibliographic record
Abstract
While Canada may be famously recognized as a bilingual country, the reality is that the rich linguistic diversity encountered on this land long predates European colonization. Through centuries of genocide, forced assimilation, and attempted erasure, many Indigenous languages live on despite the best efforts of the Canadian state. Today, as Canada claims to be on a path of reconciliation, the hierarchy of the Official Languages over Indigenous languages is perpetuated through policies that inhibit Indigenous language revitalization efforts. To remedy this, Canada should build a framework that provides Indigenous Nations and communities with adequate support to protect and revitalize their languages. This capstone analyzes select language policies at the international, federal, and provincial/territorial level to identify promising approaches to language recognition and revitalization. It then outlines three policy alternatives to address the legislative gaps: the status quo, granting Cree and Inuktitut Official Language status, and establishing Regional First Languages. These three alternatives are then evaluated according to four important criteria: recognition, access, timeliness and acceptability to Official Language minority groups. Based on this analysis, it recommends the establishment of Regional First Languages, and concludes that the federal government should provide more capacity and resources to Indigenous Nations and communities for Indigenous-led revitalization efforts.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".