Bibliographic record
Abstract
An enduring debate in the field of Arctic archaeology has been the extent to which climate change impacted cultural developments in the past. Long-term culture change across the circumpolar Arctic was often highly dynamic, with episodes of rapid migration, regional abandonment, and—in some cases—the disappearance or wholesale replacement of entire cultural traditions. By the 1960s, researchers were exploring the possibility that warming episodes had positive effects on cold-adapted premodern peoples in the Arctic by ( a) reducing the extent of sea ice, ( b) expanding the size and range of marine mammal populations, and ( c) opening new waterways and hunting areas for marine-adapted human groups. Although monocausal climatic arguments for change are now regarded as overly simplistic, the growing threat of contemporary Arctic warming to Indigenous livelihoods has given wider relevance to research into long-term culture–climate interactions. With their capacity to examine deeper cultural responses to climate change, archaeologists are in a unique position to generate human-scale climate adaptation insights that may inform future planning and mitigation efforts. The exceptionally well-preserved cultural and paleo-ecological sequences of the Arctic make it one of the best-suited regions on Earth to address such problems. Ironically, while archaeologists employ an exciting and highly promising new generation of methods and approaches to examine long-term fragility and resilience in Arctic social-ecological systems, many of these frozen paleo-societal archives are fast disappearing due to anthropogenic warming.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".