Decolonizing the digital landscape: the role of technology in Indigenous language revitalization
Bibliographic record
Abstract
Due to colonization and imperialism, Indigenous languages continue to be threatened and endangered. Resources to learn Indigenous languages are often severely limited, such as a lack of trained or proficient teachers. Materials which follow external standards or Western pedagogies may not meet the needs of the local community. One common goal for Indigenous language revitalization initiatives is to promote intergenerational language transmission and use in multiple social domains, such as the home. Could the use of technology assist in Indigenous language revitalization? And what would be its role? This article, emerging from ongoing research, aims to synthesize some key takeaways on the role of digital and online technologies in Indigenous language revitalization over the past three decades since the foundation of the World Wide Web in 1989. The article highlights how Indigenous communities, content creators, scholars and visionaries have contributed to an ongoing decolonization of the digital landscape.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".