Contested framings of climate change and health in the Arctic: A narrative analysis of health in Canadian government climate change policy affecting Inuit Nunangat
Bibliographic record
Abstract
Narratives are used to make sense of the world, to understand complex challenges and to imagine change. Inequity and unequal power structures are understood to be the root causes of disasters, but dominant narratives frame climate change as an ‘externalised’ threat and propose technocratic approaches to defending the status quo. This distracts from solutions that address the root causes of disaster. In Inuit Nunangat, social determinants of health include ongoing colonialism and policy, shaping Inuit experiences of climate change. This paper reports the results of a narrative analysis of Canadian governmental climate and health policy documents relevant to Inuit Nunangat between 2015 and 2021. Narratives are deconstructed and common narratives are identified, drawing from Burke’s Dramatistic Pentad. The dominant narrative identified focuses on knowledge, technological innovation and resilience, externalising the threat of climate change and proposing solutions that leverage knowledge and innovation. A second narrative highlights collective responsibility and partnership, identifying inequity as a driver of harm but not engaging with power relations when detailing solutions. A third narrative, present in fewer documents, centres sovereignty and relationships, identifies inequities and colonial policy as drivers of harm in the context of climate change, and proposes solutions that address root causes and further Indigenous sovereignty. How we tell the ‘story’ of climate change determines how we act and adapt. If dominant policy narratives distract from addressing the root causes of harm, inequities and violence will be perpetuated through inappropriate actions and missed opportunities. Narratives identified in this analysis offer other ways of telling this story.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".