Animals in Insular Archaeological Settings
Bibliographic record
Abstract
Abstract The animals that inhabited islands in the past and their relationships with the humans who settled them have long been of interest to archaeologists. This field of scholarship—island zooarchaeology, or the zooarchaeology of insular contexts—exhibits tremendous topical breadth and diverse theoretical and methodological approaches, in keeping with the cultural, geological, and ecological diversity of islands globally. Despite this diversity, common threads link many aspects of island zooarchaeological research owing to some of the integral characteristics of islands as compared to continents, including typically high rates of endemism, greater proximity to and abundance of aquatic and coastal resources, and elevated vulnerability to human and climate change impacts. In this chapter the authors discuss four research themes in the study of animals from insular archaeological settings: (1) anthropogenic impacts on animals, (2) fisheries historical ecology, (3) multispecies approaches, and (4) Indigenous ways of knowing. The four themes are important avenues of current research and offer a foundation for future lines of inquiry in zooarchaeology and island archaeology, where they may contribute to enhanced methodological practices and understanding of anthropogenic island histories. More broadly, island zooarchaeology plays an important role in prospective cross-disciplinary applications that address contemporary losses of global biodiversity and multiscalar ecosystem functions driven by human activities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 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".