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Record W2335983199 · doi:10.7183/0002-7316.81.2.294

A Wealth of Beads: Evidence for Material Wealth-Based Inequality in the Salish Sea Region, 4000–3500 Cal B.P.

2016· article· en· W2335983199 on OpenAlexaff
Gary Coupland, David Bilton, Terence Clark, Jerome S. Cybulski, Gay Frederick, Alyson Holland, Bryn Letham, Gretchen M. Williams

Bibliographic record

VenueAmerican Antiquity · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityVancouver Island UniversityUniversity of Toronto
Fundersnot available
KeywordsArchaeologyInequalityPeriod (music)Sound (geography)GeographyValue (mathematics)HistoryGeologyOceanographyArt

Abstract

fetched live from OpenAlex

Abstract Archaeologists working in the Salish Sea (Strait of Georgia and Puget Sound) region of the Pacific Northwest have unearthed human burials and non-mortuary features dated to 4000–3500 cal B.P. containing tens and even hundreds of thousands of stone and shell disc beads. Several sites are reported here, including burials recently excavated from site DjRw–14 located in the territory of the shíshálh Nation. We argue that the disc beads constituted an important form of material wealth at this time, based on the amount of labor that would have been required to produce them and the capacity for beads to accrue in value after their production. A model of material wealth-based inequality is developed for a period much older than many archaeologists working in the region have previously thought.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.398

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.393
Teacher spread0.310 · 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 designObservational
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

Citations41
Published2016
Admission routes1
Has abstractyes

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