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Record W7042826808

Power and participation: narrative framings of disaster, climate change, and health in Arctic North America

2022· dissertation· en· W7042826808 on OpenAlexaboutno aff

Bibliographic record

VenueWhite Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2022
Typedissertation
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeClimate changeArcticContext (archaeology)Government (linguistics)IndigenousNarrative inquiryCorporate governancePsychological resilience
DOInot available

Abstract

fetched live from OpenAlex

Disasters are the outcome of social, political, and economic conditions and processes, particularly in the context of climate change. However, dominant narratives of climate change and health persistently frame climate change as an external threat and driver of harm. This obscures the root causes of disaster, such as inequity, colonialism, and poor governance. There are increasing calls to shift dominant narratives on climate change to encompass the root causes of disaster, and interest in the re-telling of climate change and health narratives from Indigenous perspectives. This thesis critically analyses the root causes of disaster for Inuit in Arctic North America (a region experiencing rapid climatic change), the ways that these are addressed in narratives about climate change and health, and how these narratives are constructed. Specifically, it focuses on the ongoing disruption of time spent on the land, which is reported to impact the physical and emotional health of Inuit as a ‘creeping disaster’, and which has been linked by some to climate change. \n \nFirst, based on the ‘forensic investigations of disaster’ approach, the literature is systematically reviewed, using qualitative causal analysis, to identify the root causes of constrained mobility for Inuit in Arctic North America. It identifies barriers to time spent on the land, which are driven by processes of governance and inequality, as opposed to environmental hazards. Second, narrative analysis is used to unpack how Canadian government policy frames the problems, solutions, and responsibilities of health and climate change. Findings suggest that dominant narratives do not engage with the social determinants of health or root causes of disaster, and fail to propose solutions that address inequality, power-relations, or colonialism. Narratives that do engage with these issues are marginalised by the power of the dominant narratives, and do not appear to be shaping proposed solutions. Third, as there are suggestions that increased engagement of Indigenous Peoples in research and policymaking may pluralise these policy narratives, this thesis critically examines engagement with diverse knowledge types in research exploring climate-sensitive processes in an Arctic setting. Findings suggest that the degree of power afforded to Inuit in the research processes is frequently limited or not detailed, suggesting a need for clearer reporting and greater engagement in research design. \n \nThis thesis, therefore, argues that dominant policy narratives of climate change and health in Arctic North America conceal the root causes of harm and propose solutions that fall short of addressing them. Opportunities to shift the narrative are missed due, in part, to limited attention to power in participatory knowledge production. The intense focus on climate change as an external driver of harm in narratives about Arctic North America is problematic. In the context of climate change, action is needed that addresses the root causes of harm, including colonial legacies, power imbalances and inequities. We need shared narratives that can push us to imagine and engender this change.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0300.028
Scholarly communication0.0100.007
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.043
GPT teacher head0.313
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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