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Record W2567334362 · doi:10.3764/aja.121.1.0061

From Formal to Technical Styles: Production Challenges and Economic Implications of Changing Tableware Styles in Roman to Late Antique Sagalassos

2016· article· en· W2567334362 on OpenAlexfundno aff
Elizabeth A. Murphy, Jeroen Poblome

Bibliographic record

VenueAmerican Journal of Archaeology · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Mediterranean Archaeology and History
Canadian institutionsnot available
FundersFonds Wetenschappelijk OnderzoekVlaamse regeringYork University
KeywordsAntiqueProduction (economics)ArtGeographyArchaeologyEconomics

Abstract

fetched live from OpenAlex

Changing tableware styles between the Roman Imperial and Late Antique periods have attracted significant attention recently, with socially constructed interpretations of consumer demand that view changing vessel shapes, sizes, and decoration in relation to communal dining practices of late antiquity. Building on that research, this study approaches such stylistic changes from the perspective of the important, yet less investigated, figure of the producer on the workshop floor. In comparing two production dumps from the tableware production center at Sagalassos (southwest Turkey), one dated to the second century C.E. and one to the late fifth to early sixth century C.E., this study identifies shifts in the technical and technological styles between the two periods that relate to changes in vessel form, size, and finishing. Having identified several critical technical changes in the Late Antique production at Sagalassos, this article then explores the degree to which changing vessel styles affected manufacturing output, production organization, and workshop economy, consequently demonstrating the dynamic response by an ancient local industry to changing technical and social contexts through several centuries of activity. Using detailed classifications and quantifications of manufacturing waste, this research also develops new methodologies for the analysis of ancient production sites.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.705
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.238
Teacher spread0.218 · 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 teacher head, 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

Citations15
Published2016
Admission routes1
Has abstractyes

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