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WOAD, TATTOOING AND IDENTITY IN LATER IRON AGE AND EARLY ROMAN BRITAIN

2005· article· en· W2129389142 on OpenAlexfundno aff
Gillian Carr

Bibliographic record

VenueOxford Journal of Archaeology · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
FundersYork University
KeywordsPaintingIdentity (music)Style (visual arts)Construct (python library)ArtFace (sociological concept)IndigenousVisual artsClothingShot (pellet)HistoryAestheticsArchaeologySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

Summary. This paper explores the archaeological evidence for the practice of facial and corporeal dyeing, painting and tattooing in the later Iron Age and early Roman period. The aim is to construct a hypothesis which explains how, why, when and by whom such pigments were worn. Although this hypothesis discusses woad‐derived indigo, this is used mainly, although not exclusively, as an experimental tool, as no conclusive archaeological evidence exists which reveals the identity of the ‘real’ pigment(s). Woad has also long held a place in the popular imagination as the source of the dye which the ancient Britons used to paint themselves. This paper explores the possibility that the cosmetic grinder was the focal artefact used in body painting or tattooing, and was used for grinding and mixing body and face paint. It is suggested that, rather than being a ‘Roman’‐style tool for cosmetic application from the start, it may have begun life as an artefact first used by the later Iron Age Britons for body painting and expressing indigenous identities.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.010
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.292
Teacher spread0.282 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

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