Progress in climate change adaptation in the Arctic
Bibliographic record
Abstract
Abstract Climate adaptation is a priority for Arctic regions which are witnessing some of the most rapid warming globally. Studies have documented examples of adaptation responses in the Arctic, but assessments evaluating if and how progress is being made over time remain scarce. We identify and examine adaptation progress in the Arctic using a systematic tracking methodology to compare adaptations documented during 2014–19 to those documented for the period 2004–2013 in a benchmark study by Ford et al (2014). Utilising the peer reviewed literature as out data source, we find no noticeable increase in reported adaptations across the two time periods, with the profile of adaptations undertaken remaining largely the same. The majority of documented adaptations continue to be reported in North America, are being undertaken most often in the subsistence-based hunting and fishing sector, are primarily developed in response to a combination of climatic and non-climatic stimuli, are reactive and behavioural in nature, and are mainly carried out at the individual/community scale. Climate change is observed, however, to have a more prominent role in motivating adaptation between 2014–19, consistent with intensifying climate-related exposures in the Arctic. There is limited evidence in the reported adaptations analysed that potential opportunities and benefits from the impacts of climate change are being targeted. The paper provides a general characterisation of adaptation across the Arctic and how it is evolving, and needs to be complimented in follow-up work by studies using alternative data sources on adaptation and research at national to regional scales.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".