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Purity, Pollution, and Systems of Classification

2017· other· en· W2912859797 on OpenAlexaff
Gregory Forth

Bibliographic record

VenueThe International Encyclopedia of Anthropology · 2017
Typeother
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTabooIdeologyEpistemologyMAGIC (telescope)SociologyValue (mathematics)Variety (cybernetics)Social sciencePhilosophyMathematicsComputer sciencePolitical scienceAnthropologyArtificial intelligencePoliticsLaw

Abstract

fetched live from OpenAlex

Ideas of purity express a value for keeping different categories of things—be they substances, natural kinds, or social statuses—separate from one another, thereby avoiding consequent impurity or pollution. Necessarily, therefore, notions of purity and pollution are connected to systems of classification that define the culturally recognized categories to which purity rules apply. Issues of pollution, as well as the closely related concept of taboo, have been treated by a variety of anthropological theorists, from Frazer to Durkheim and from Douglas and Dumont to Valeri. But, while these authors have mainly been concerned with practices and ideologies pertaining to religion and magic, it can further be shown how notions of purity also inhere in the classifying behavior of both folk biologists and international scientific taxonomists, and, moreover, how they relate to panhuman classificatory practices of interest to cognitive anthropologists, psychologists, and linguists.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.016
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0070.048
Scholarly communication0.0160.018
Open science0.0020.009
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.044
GPT teacher head0.395
Teacher spread0.351 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2017
Admission routes1
Has abstractyes

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