Futurity of Indigenous Languages Found in White Settler Nation-States
Bibliographic record
Abstract
This chapter raises issues pertaining to language policies in Canada, as it historically and politically has been used as a tool of erasure, to implement and perpetuate white settler narratives. Canadian language policies inherit colonial perspectives, historically and continuously function to create severe racial divisions through forced assimilation. The conceptualization and current discourses of language policies in Canada can best be understood through an anti-racist and anti-colonial theoretical framework. This chapter explores Canada’s assimilationist laws and policies, which have had profound impacts on Indigenous peoples and their ability to preserve their Indigenous languages. This chapter looks at residential schools as a key part of breaking down communities, and as a direct measure made by the Canadian government, to deteriorate Indigenous peoples’ cultural identities and knowledges, through language erasure. Additionally, this chapter will examine the forced imposition of a Eurocentric curriculum onto Indigenous communities, which continues to divert and disrupt Indigenous languages, as the Eurocentric curriculum inherently embodies neo-colonial narratives that subjugate and disrupt Indigenous knowledge systems. With this, I am declaring that white supremacist ideologies and practices continue to be found in classrooms, despite claims of inclusivity, encompassing multicultural pedagogies in Canada. In the last section of the chapter, I will discuss decolonization as knowledge activism, and how we must work to disrupt colonial and neo-colonial agendas found in current discourses of our education systems, which continue to centralize English as the only formal language, while denouncing all “others”.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".