Untold Stories: Indigenous Knowledge Beyond the Changing Arctic Cryosphere
Bibliographic record
Abstract
Scientific attention to climate change in the Arctic has spurred extensive research, including many studies of Indigenous knowledge and the effects of climate change on Indigenous peoples. These topics have been reported in many scientific papers, books, and in the IPCC's 2019 Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC), as well as attracting considerable interest in the popular media. We assembled a set of peer-reviewed publications concerning Arctic Indigenous peoples and climate change for the SROCC, to which we have added additional papers discovered through a subsequent literature search. A closer look at the 76 papers in our sample reveals additional emphases on economics, culture, health and mental health, policy and governance, and other topics. While these emphases reflect to some degree the perspectives of the Indigenous peoples involved in the studies, they are also subject to bias from the interests and abilities of the researchers involved, compounded by a lack of comparative research. Our review shows first that climate change does not occur in isolation or even as the primary threat to Indigenous well-being in the Arctic, but the lack of systematic investigation hampers any effort to assess the role of other factors in a comprehensive manner; and second that the common and perhaps prevailing narrative that climate change spells inevitable doom for Arctic Indigenous peoples is contrary to their own narratives of response and resilience. We suggest that there should be a systematic effort in partnership with Indigenous peoples to identify thematic and regional gaps in coverage, supported by targeted funding to fill such gaps. Such an effort may also require recruiting additional researchers with the necessary expertise and providing opportunities for inter-regional information sharing by Arctic Indigenous peoples. As researchers who are visitors to the Arctic, we do not claim that our findings are representative of Indigenous perspectives, only that a more accurate and comprehensive picture of Arctic Indigenous peoples' knowledge of and experiences with climate change is needed. Our analysis also reflects some of the SROCC knowledge gaps and the conclusions provide suggestions for future research.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".