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Animals in Insular Archaeological Settings

2025· book-chapter· en· W4413395218 on OpenAlexaff
Michelle J. LeFebvre, Melinda S. Allen, Tiina Manne, Kellie Pollard, Damià Ramis, Christina M. Giovas

Bibliographic record

VenueOxford University Press eBooks · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArchaeologyGeographyHistoryArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.244
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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