32. Using Our Languages Improves Our Health (I)
Bibliographic record
Abstract
During this workshop, together we'll become aware of connections between language use and wellbeing. We'll talk about how different cultures define health. (“Western Desert Aborigines see 'health' not only in the absence of sickness, but more positively: on the physical side, in the presence of coldness and dryness in the body, and on the spiritual side, in having one’s spirit in the area of the stomach. Conversely, when the spirit moves to the back of a person or leaves the body altogether, when the body is hot and wet, then the person is considered sick.” Peile, A. R. 1997 Body and Soul: An Aboriginal View. Victoria Park, WA: Hesperian Press. p. xxi.) We'll review some case studies connecting ancestral language use to rates of diabetes, suicide, and substance abuse. (Oster, Richard, Angela Grier, Rick Lightning, Maria J. Mayan, and Ellen L. Toth. 2014. Cultural continuity, traditional Indigenous language, and diabetes in Alberta First Nations: a mixed methods study. International Journal for Equity in Health 13(92). (doi:10.1186/s12939-014-0092-4)) (Hallett, D., Chandler, M. J., & Lalonde, C. (2007). Aboriginal Language Knowledge and Youth Suicide. Cognitive Development 22 (3), pp. 392–399.) (Chandler, M. J. & Lalonde, C. E. (2008). Cultural Continuity as a Protective Factor against Suicide in First Nations Youth. Horizons --A Special Issue on Aboriginal Youth, Hope or Heartbreak: Aboriginal Youth and Canada’s Future. 10(1), 68-72.)) Weʼll share eye witness accounts of language use and its relation to health from our own and othersʼ experiences. Weʼll consider the difference between correlation and causation. Weʼll consider various health factors; emotional, physical, social, spiritual, economic, educational and how we might collect evidence-based data to support language-health causation. Weʼll practice our arguments to authorities [family, community, legislature, health industry, congress] to support ancestral Indigenous language based on the health benefits it provides.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.113 | 0.086 |
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".