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Record W2760780753 · doi:10.1017/s0959774317000592

Death and the City: The Cemeteries of Amarna in Their Urban Context

2017· article· en· W2760780753 on OpenAlexaff
Anna Stevens

Bibliographic record

VenueCambridge Archaeological Journal · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeology and Historical Studies
Canadian institutionsArthur B. McDonald-Canadian Astroparticle Physics Research Institute
FundersPasold Research FundUniversity of ArkansasUnited States Agency for International DevelopmentBritish AcademyMcDonald Institute for Archaeological ResearchNational Geographic SocietyNational Endowment for the Humanities
KeywordsSettlement (finance)ArchaeologyContext (archaeology)ExcavationHistoryCharacter (mathematics)Ancient cityUrban landscapeGeographyPopulationAncient historySociologyDemographyEnvironmental planning

Abstract

fetched live from OpenAlex

Burial grounds are increasingly being considered as components of lived urban environments in the past. This paper considers how the ancient Egyptian city of Akhetaten , built by king Akhenaten ( c. 1349–1332 bc ), was constructed and experienced as a space inhabited both by the living and the dead. Drawing upon results from ongoing excavations at the burial grounds of the general population, it considers how the archaeological record of the settlement and its cemeteries segue and explores how the nature of burial landscapes and the need to maintain reflexive relationships between the living and the dead in the midst of a changing religious milieu contributed to the unique character of Akhetaten as a city. It asks what kind of city Akhetaten was, and what it was like to live through the Amarna period.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.015
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.248
Teacher spread0.174 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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