"We can do our own thing here on Haida Gwaii": The Haida Nation's response to COVID-19
Bibliographic record
Abstract
The Council of the Haida Nation (CHN) is the National government of all Haida citizens—and their response to the COVID-19 pandemic on Haida Gwaii—is the central focus of this study. The CHN’s response is contextualized through an analysis of governance structures, consideration of previous epidemics, diseases, and health inequalities. The research questions for this project include: (1) How did the CHN’s role shift during the COVID-19 emergency response on Haida Gwaii; (2) What lessons can be garnered from the CHN’s response to inform future Haida Nation governance? To explore these research questions I conducted semi-structured, in-depth interviews with a sample of seven people who were living on Haida Gwaii during the pandemic and had some involvement with the CHN. Following an iterative process of data analysis, four main themes emerged from the data. These themes encompassed the inclusive approach taken by the CHN, the tireless work a small group of people did, and the importance of jurisdiction and self-determination while also considering lessons learned and capacity. The findings demonstrated the importance of continued pushes for self-determination as well as the ability of the CHN to expand its governance role.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".