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Record W2298170170 · doi:10.1017/cbo9780511542534

Human Biologists in the Archives

2002· book· en· W2298170170 on OpenAlexaff
D. Ann Herring, Alan C. Swedlund

Bibliographic record

VenueCambridge University Press eBooks · 2002
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVariety (cybernetics)Field (mathematics)HistoryHistorical recordGenealogyData scienceGeographyComputer scienceArt history

Abstract

fetched live from OpenAlex

Many physical anthropologists study populations using data that come primarily from the historical record. For this volume's authors, the classic anthropological 'field' is not the glamour of an exotic locale, but the sometimes tedium of the dusty back rooms of libraries, archives and museum collections. This book tells of the way in which archival data inform anthropological questions about human biology and health. The authors present a diverse array of human biological evidence from a variety of sources including the archaeological record, medical collections, church records, contemporary health and growth data and genetic information from the descendants of historical populations. The papers demonstrate how the analysis of historical documents expands the horizons of research in human biology, extends the longitudinal analysis of microevolutionary and social processes into the present and enhances our understanding of the human condition.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.067
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 science0.0010.000
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 head0.019
GPT teacher head0.214
Teacher spread0.195 · 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 designNot applicable
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

Citations23
Published2002
Admission routes1
Has abstractyes

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