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Record W4385249399 · doi:10.1016/j.jaa.2023.101533

New fingerprint evidence for female potters in Late Bronze Age Canaan: The demographics of potters and division of labour at Tel Burna

2023· article· en· W4385249399 on OpenAlexaff
Jon Ross, Kent D. Fowler, Itzhaq Shai

Bibliographic record

VenueJournal of Anthropological Archaeology · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Near East History
Canadian institutionsUniversity of Manitoba
FundersAriel UniversityIsrael Science Foundation
KeywordsBronze AgeDemographicsArchaeologyHistoryAncient historyGeographyDemographySociology

Abstract

fetched live from OpenAlex

Techno-stylistic studies in ceramic analysis have largely focused on characterising production groups, based on the similarity of various objects and how they were made. The demographics of potters and the division of labour often remain enigmatic in current chaîne opératoire research. A growing number of biometric studies have demonstrated the potential of fingerprints preserved on ceramic surfaces for classifying the age and sex of potters. In this paper, we use a recently introduced identification matrix to model labour divisions based on 52 fingerprints preserved on a diverse range of objects from the Late Bronze Age II stratum at Tel Burna. The sample includes objects from the recently exposed cultic enclosure. Based on broad ethnographic considerations, women were the principal potters in Canaanite society. Our study tests this hypothesis with regards to who made pottery for cultic use. We identify patterns in age categories and a sexual division of labour for the manufacture of select objects and vessel types. The results lead us to discuss possible effects of imperialism on labour organisation. We provide the first compelling insights into the social relations of pottery production at a time when Egypt exercised hegemony over the city-states of the Southern Levant .

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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.317
Teacher spread0.222 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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