Archéologies du changement climatique: Perceptions et perspectives
Bibliographic record
Abstract
Climate change is the biggest challenge facing humanity today, and discussions of its effects—from habitat loss to psychological impacts—can be found in most academic disciplines. Among the many casualties of contemporary climatic change is the archaeological heritage of Arctic and subarctic regions, as warming, erratic weather patterns, coastal erosion, and melting permafrost threaten the anthropogenic and ecological records found in northern environments. Archaeology is uniquely positioned to provide long-term perspectives on human responses to climatic shifts, and to inform on the current debate. In addition, the practice of archaeological research and assimilation of archaeological heritage into contemporary society can also address or even mitigate some of the sociocultural impacts of climate change. Focusing on the Yup’ik communities and critically endangered archaeology of the Yukon–Kuskokwim (Y–K) Delta, Alaska, here we argue community archaeology can provide new contexts for encountering and documenting the past, and through this, reinforce cultural engagement and shared cultural resilience. We emphasize the benefits of archaeological heritage and the practice of archaeology in mitigating some of the social and psychological impacts of global climate change for communities as well as individuals. We also propose that archaeology can have a role in reducing psychological distance of climate change, an acknowledged barrier that limits climate change action, mitigation, and adaptation, particularly in regions where the impacts of contemporary climate change have not yet been immediately felt.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".