“What is language for us?”: Community-based Anishinaabemowin language planning using TEK-nology
Bibliographic record
Abstract
Abstract Language planning and policy (LPP), as a field of research, emerged to solve the “problem” of multilingualism in newly independent nation-states. LPP’s principal emphasis was the reproduction of one-state, one-language policies. Indigenous languages were systematically erased through top-down, colonial medium-of-instruction policies, such as in Canadian residential schools. To this day, ideologies and policies still privilege dominant classes and languages at the expense of Indigenous and minoritized groups and languages. To prevent further erasure and marginalization, work is required at multiple levels. There is growing consensus that top-down, government-led LPP must occur alongside community-led, bottom-up LPP. One shared and common goal for Indigenous language reclamation and revitalization initiatives across the globe is to promote intergenerational language transmission in the home, the community, and beyond. The affordances of digital and online technologies are also being explored to foster more self-determined virtual communities of practice. Following an Indigenous research paradigm, this paper introduces theTEK-nology(Traditional Ecological Knowledge [TEK] and technology) pilot project in the Canadian context.TEK-nologyis an immersive, community-led, and technology-enabled Indigenous language acquisition approach to support Anishinaabemowin language revitalization and reclamation. TheTEK-nologypilot project is an example of bottom-up, community-based language planning (CBLP) where Indigenous community members are the language-related decision-makers. This paper demonstrates that Indigenous-led, praxis-driven CBLP, usingTEK-nology, can support Anishinaabemowin language revitalization and reclamation and more equitable, self-determined LPP. The CBLPTEK-nologyproject has implications for status and acquisition language planning; culturally responsive LPP methodologies; and federal, provincial, territorial, and family language policy.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".