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Record W2587878413 · doi:10.1017/eaa.2017.12

What Are Bucrania Doing in Tombs? Art and Agency in Neolithic Sardinia and Traditional South-East Asia

2017· article· en· W2587878413 on OpenAlexfundno aff
Guillaume Robin

Bibliographic record

VenueEuropean Journal of Archaeology · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicPaleopathology and ancient diseases
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueUniversity of EdinburghNational University of SingaporeUniversity of CambridgeUniversity of South FloridaSimon Fraser UniversityUniversity of MichiganUniversity of Southern California
KeywordsDivinityAgency (philosophy)ArchaeologyAncient historyCentral asiaEthnographySocial lifeHistoryGeographyEthnologySociology

Abstract

fetched live from OpenAlex

The interior of Neolithic tombs in Europe is frequently decorated with carved and painted motifs. In Sardinia (Italy), 116 rock-cut tombs have their walls covered with bucrania (schematic depictions of cattle head and horns), which have long been interpreted as representations of a bull-like divinity. This article reviews similar examples of bucranium ‘art’ in the tombs of three traditional societies in South-East Asia, focusing on the agency of the motifs and their roles within social relationships between the living, the dead, and the spiritual world. From these ethnographic examples and the archaeological evidence in Sardinia, it is suggested that bucrania in Neolithic tombs were a specialized form of material culture that had multiple, cumulative effects and functions associated with social display, memory, reproduction, death, and protection.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.240
Teacher spread0.185 · 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
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

Citations9
Published2017
Admission routes1
Has abstractyes

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