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Record W4399384970 · doi:10.1515/itit-2023-0062

Three degrees of separation: networks in the city of Babylon during the Reign of Darius I (522–486 BCE)

2024· article· en· W4399384970 on OpenAlexaff
Jinyan Wang

Bibliographic record

Venueit - Information Technology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBetweenness centralityReignCentralityPosition (finance)Power (physics)GenealogyEconomic geographySociologyAncient historyGeographyEconomyHistoryBusinessPolitical scienceMathematicsCombinatoricsEconomicsLawPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper, I reconstruct the networks of Babylonian urban dwellers during the reign of Darius I (522–486 BCE) based on 803 tablets from 10 private archives in Babylon. The main aim is to examine the structure and connectivity of the network that connected different urban families and groups of individuals outside the families. I focus on the positions individuals occupied within the network that yielded them the power to connect smaller parts of the network. The first approach used to identify and analyze these positions is the betweenness centrality measure. The second approach is the analytic concept of brokerage, the role of mediating between two or more individuals or communities that would otherwise have no connection to each other. I identify differences in the ways that the intermediate position of brokers affected the formation of the network. These brokerage roles resulted from families’ strategies to increase their household wealth by constructing and optimizing marriage, prebendary, and business relations.

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)0.002
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 score0.258
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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
Published2024
Admission routes1
Has abstractyes

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