Language reclamation and mental health: Revivalistics in the service of the wellbeing of Indigenous people
Bibliographic record
Abstract
Language is postulated as core to a people’s wellbeing and mental health. Hallett, Chandler and Lalonde (2007) report a clear correlation between youth suicide and lack of conversational knowledge in the native language in British Columbia, Canada. However, there has been no systematic study of the impact of language revival (in contrast to language loss) on mental health, partly because language reclamation is still rare. The Barngarla people of Eyre Peninsula, South Australia are but one example of Aboriginal and Torres Strait Islander peoples suffering the effects of linguicide (language killing). Their dependency on the coloniser’s tongue, language loss, and consequent lack of cultural autonomy and intellectual sovereignty, increase the phenomenon of disempowerment, self-loathing and suicide. According to the 2008 National Australian Torres Strait Islander (ATSI) Social Survey (Australian Bureau of Statistics 2010a), 31% of Indigenous Australians aged 15+ experienced high or very high levels of psychological distress in the four weeks prior to their interview. This is 2.5 times the rate for non- Indigenous Australians. The Barngarla people have decided to reclaim their “sleeping beauty” tongue. While looking at evidence from Barngarla and other Aboriginal revivals, this paper begins to determine whether there is a positive correlation between language reclamation and increased personal empowerment, improved sense of identity and purpose as well as reduced cases of depression. Acknowledgments: Caryn Rogers, Amy Finlay, Michael Wright, Leonie Segal, and Gareth Furber.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".