Living Language, Resurgent Radio: A Survey of Indigenous Language Broadcasting Initiatives
Bibliographic record
Abstract
For a demise that has been predicted for over 60 years, radio is a remarkably resilient communications medium, and one that warrants deeper examination as a vehicle for the revitalization of historically marginalized and Indigenous languages. Radio has not been eroded by the rise of new media, whether that be television, video, or newer multimodal technologies associated with the internet. To the contrary, communities are leveraging the formerly analogue medium of radio in transformative ways, breathing new life into old transistors, and using radio for the transmission of stories, song, and conversation. In this contribution, we highlight effective and imaginative uses of radio for Indigenous language reclamation through a series of case studies, and we offer a preliminary analysis of the structural conditions that can both support and impede developments in Indigenous-language radio programming. The success of radio for Indigenous language programming is thanks to the comparatively low cost of operations, its asynchronous nature that supports programs to be consumed at any time (through repeats, podcasts, downloads, and streaming services) and the unusual, even unique, quality of radio being both engaging yet not all-consuming, meaning that a listener can be actively involved in another activity at the same time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".