A decadal reanalysis of climate vulnerability in the Canadian Arctic: the case of Arctic Bay
Bibliographic record
Abstract
The Arctic is widely acknowledged as a global hotspot of climate change impacts. The implications of these changes are particularly pronounced for the Indigenous populations living in arctic regions, whose close association with and dependence on the land, sea, ice, and natural resources increases their sensitivity to climate-related risks. The past decade has seen the rapid expansion of research assessing these risks, which has increased our understanding of how climate change interacts with non-climatic drivers of vulnerability and resilience to affect human society. However, our understanding of the dynamic nature of vulnerability and its determinants over time remains incomplete: while scholarship has developed a baseline and generalized understanding of the human dimensions of climate change, little is known of the long-term dynamics in the context of continuing environmental, economic and societal change. This thesis contributes to the development of a dynamic understanding of the processes and conditions that influence climate change vulnerability over time by conducting a decadal restudy of Ford et al (2006) in Ikpiarjuk (Arctic Bay), Nunavut. Using a research methodology consistent with the first study, and focusing on risks associated with subsistence harvesting activities, participant observation and semi-structured interviews were conducted in 2015 with 40 participants. Comparing this data to the original data collected in 2004, the thesis finds changes in the biophysical environment have continued and accelerated in many instances over the last decade. Within this context, socio-economic conditions have shaped how the community is experiencing climate change, both exacerbating and abating associated risks. It is found that the increased availability and accessibility of new technologies (predominantly Internet connection and GPS devices) is driving adaptive capacity in the community. In the same way, previous vulnerability assessments have suggested that changes to traditional sharing networks may hinder a community's adaptive capacity. Here, these changes are found to be evolving in ways that facilitate adaptation to both environmental and economic stress.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".