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Record W3110101088 · doi:10.15402/esj.v6i1.70741

Perspectives on Health: Working with Communities as Cultural Anthropologists and Bioarchaeologists

2020· article· en· W3110101088 on OpenAlexvenueno aff
Samantha Purhcase

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRace, Genetics, and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBioarchaeologySociologyAnthropologyRelevance (law)Cultural anthropologyExtant taxonPoliticsApplied anthropologyCommunity studiesEcological anthropologyNatural (archaeology)Social scienceGeographyHistoryPolitical scienceArchaeologyAnthropology of art

Abstract

fetched live from OpenAlex


 The anthropological study of health has always been an integral part of the discipline. With the development of cultural anthropology and physical anthropology (specifically, bioarchaeology) in the nineteenth century came different theories and methodologies concerning the study and definition of communities. Still today, cultural anthropology and bioarchaeology share the same broad goals of exploring the evolving relationships between experiences of health and the community, culture, and environment (being natural, domestic, political, and social). That cultural anthropologists study extant cultures and bioarchaeologists do not has necessitated the evolution of different methodological practices. Here, I explore some of the differences between these two sub-disciplines: their differing notions of community, how they engage with communities, and the relevance of their work to the communities they study. I contextualize this analysis with a short discussion of the sub-disciplines’ co-evolution and ground it with examples from my research with middle Holocene Siberian, Russian Federation, and Anglo-Saxon to Post-Industrial British communities.

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)0.279
metaresearch head score (Gemma)0.152
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2790.152
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.2550.002
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.364
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 head0.161
GPT teacher head0.397
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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