Promoting Health Resiliency during COVID-19 Disaster: The Relevance of Indigenous Land-based Practice (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED The COVID-19 pandemic, like a natural disaster, the COVID-19 pandemic has had a significant effect on the vulnerable portion of society, particularly on Indigenous and visible minority immigrants in Canada. While Indigenous and visible minority people are very diverse and experienced the impact of Covid-19 very differently, both groups have a significant lack of equal access to pandemic resiliency. As a visible minority immigrant family in Indigenous land in Treaty 6 territory, we (as a colour settler family in Indigenous land known as Canada) learned Indigenous land-based education (ILBE) from Indigenous Elders and Knowledge-keeper's land-based stories, traditional knowledge, resiliency, and practice. We have been learning and practicing ILBE to develop resiliency during a natural disaster, such as during the COVID-19 pandemic. We used a land-based learning as a research methodology for learning health wellness from land. We discussed why ILBE matters for building resiliency, resistance, and self-determination within a family and community; how can it help others? We have seen how COVID-19 has created severe negative impacts on mental and physical health. During the high climate change era, many pandemics are yet to come. However, the ILBE can offer us many opportunities to build our resistance and resiliency through decolonizing our ways of knowing and doing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".