Indigenous language revitalization using <i>TEK-nology</i> : how can traditional ecological knowledge (TEK) and technology support intergenerational language transmission?
Bibliographic record
Abstract
Indigenous communities worldwide face threats to their linguistic and epistemic heritage with the unabated spread of dominant colonial languages and global monocultures, such as English and the neoliberal, imperialistic worldview. There is considerable strain on the relatively few Elders and speakers of Indigenous languages to maintain cultures and languages decimated by centuries of colonialism. One shared and common goal for Indigenous language revitalization initiatives is to reinvigorate intergenerational language transmission in the home, the community and beyond in as many ways as possible. How can technology support this nuanced process and existing initiatives? Following an Indigenous research paradigm, this article explores an immersive, community-led Indigenous language acquisition approach – TEK-nology (traditional ecological knowledge [TEK] and technology) – to support Anishinaabemowin language revitalization and reclamation (ALRR) in the Canadian context. The TEK-nology pilot project identifies (1) the impacts of centring Indigenous worldviews in technology, language learning and teaching; (2) how we can develop and co-create technology-enabled, culturally and environmentally responsive pedagogies and (3) the important implications of decolonizing language education for Indigenous and majority languages. The TEK-nology pilot project demonstrates how community-led, relational technology and immersive Indigenous language acquisition can support ALRR and foster more equitable multicultural and multilingual education practice and policy.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".