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Record W4205491840 · doi:10.1558/jma.21977

Same Language, Different Diet

2022· article· en· W4205491840 on OpenAlexaff
Alejandro G. Sinner, Ariadna Nieto-Espinet, Sílvia Valenzuela

Bibliographic record

VenueJournal of Mediterranean Archaeology · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeological and Historical Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousPaceGeographyArchaeologySettlement (finance)HistoryColonialismOnomasticsEcology

Abstract

fetched live from OpenAlex

This study uses faunal and epigraphic evidence from the valley of Cabrera de Mar in present-day Catalonia (Spain) as proxies for understanding complex processes and dynamics of cultural change between the late Iron Age and early Roman times. The faunal remains indicate significant dietary change, although the epigraphic evidence implies that language—in contrast—changed at a slower pace, as shown by the use of indigenous onomastics and the continued use of the Iberian script, coin legends included. To ensure an interdisciplinary analysis, the study also discusses change as perceptible in architectural remains, ceramics and funerary practices. Our study shows that cultural change can take place at different levels and according to different rhythms, not only on regional and settlement planes but also at neighbourhood and household scales. Finally, our results highlight the value of archaeology as a tool for studying and understanding colonial encounters.

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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.249
Teacher spread0.204 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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