Stories of empowerment, resilience and healing
Bibliographic record
Abstract
Indigenous peoples of Québec, such as the Inuit and Kanien&s;kehá:ka, have been exposed to traumatic experiences similar to other Indigenous groups all over the world. These populations were impacted by dispossession, disempowerment, and colonization histories. They acknowledge a need to heal from the past, the socio-economic disadvantages, and health inequalities they have endured. Despite the difficulties experienced by these populations, they have their own longstanding traditions of healing and resilience which are expressed through stories, ceremonies, and local languages. The resilience of the Inuit has been widely recognized, especially with regard to their ability to adapt to the difficult physical environment of the Arctic. We present the results of a multidisciplinary project conducted in collaboration with members of two Indigenous communities and their supporters in Québec. Through qualitative and participatory research methods, we have collected and analyzed the perspectives and narratives of people who work in community and mental health settings with regard to instruments for measuring, improving and treating mental health. This project is an example of building bridges between members and groups of Indigenous communities, academics and institutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".