In Their Words
Bibliographic record
Abstract
Yukon University and the Vuntut Gwitchin First Nation (VGFN) explored emerging issues during the COVID-19 pandemic within the community of Old Crow. This community-based participatory research project took place in Old Crow, Yukon and sought to hear the perspectives of citizens of the community during the pandemic. Using a strengths-based approach grounded in Indigenous ways of knowing and doing, participants described the impacts of COVID-19 in the northern Village of Old Crow on intergenerational trauma, mental wellness, social divisions created by vaccine uptake, and social isolation in an already isolated community. We also sought to learn how the health and wellness of the Vuntut Gwitchin citizens was impacted, including but not limited to, gender, the effects of COVID-19, vaccine confidence, social divisions generated through personal vaccine decisions, mental health and substance use, and the impact of long COVID. We heard how the community mobilized and reacted to the pandemic through policies and decisions, as well as programs and support offered to citizens. This project identified the lessons learned in the response to COVID-19 that could guide the response to subsequent pandemics or health emergencies that are culturally safe and strengthen the capacity of the community, as well as the health and wellness of the citizens. The participants’ perspectives reflected their resiliency, self-determination, strong sense of community, and traditional ways of knowing and being. The uniqueness of their experiences may provide insights that can support other communities that are Indigenous, rural and remote in dealing with future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.292 | 0.202 |
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".