Recent Advances in the Archaeology of the Arctic Coasts of Alaska, Canada, and Greenland
Bibliographic record
Abstract
Abstract In this chapter the authors use a decade (2013–2022) of published works (N = 288) to characterize recent archaeological research in the coastal North American Arctic. While zooarchaeology dominates one-quarter of all coastal arctic research (including hunting and butchering methods, raw-material selection, bone-tool production and use, cosmology, and human–animal relationships), the use of archaeometric methods to answer Arctic questions has notably increased. These include genetic analysis of ancient humans (and their dogs) to confirm traditional archaeological understandings of migration and settlement by pre-Inuit and Inuit populations across the Arctic, stable isotope analysis to reconstruct diet and paleoecological conditions, clay sourcing and pottery residue analysis, and Bayesian modeling of radiocarbon dates as proxies for population size and movement. Although traditional culture historical research still continues, Arctic archaeology has moved from “lone-wolf” field exploration to more diverse and collaborative research projects to take advantage of new laboratory methods. The most positive change over this decade has been the inclusion of Indigenous communities as active members of archaeological research programs. Unfortunately, the greatest threat to archaeological research and heritage management across the North American Arctic is modern climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".