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Record W4406195419 · doi:10.1016/j.trpro.2024.12.023

Reconstructing the Transport Network of Ancient China and its Relationship to Social Networks

2025· article· en· W4406195419 on OpenAlexfundno aff
Wenlong Li, Jan‐Dirk Schmöcker, Liang Zhao

Bibliographic record

VenueTransportation research procedia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaHonjo International Scholarship FoundationAcademia SinicaUK Research and Innovation
KeywordsChinaComputer scienceTransport engineeringBusinessGeographyEngineeringArchaeology

Abstract

fetched live from OpenAlex

This study reconstructs ancient China's transport system, exploring its intersection with societal networks. We begin by procuring population data indicative of years 2, 742, 1102, and 1522. The transportation matrix, inclusive of roads, rivers, and canals, is subsequently reconstituted. In continuation, we ascertain the transport accessibility for heavy goods. Our accessibility model reveals that history lends itself to a more coherent explanation through quantitative research. We then deploy a gravity model to examine the correlation between transport systems and social networks. The model delineates how transport, particularly travel duration, influenced societal interconnections. The implemented gravity model further highlights the steady deterrent of travel time, suggesting that transport infrastructure might sustainably govern social networks on a historic spectrum. Finally, our research draws on the analysis of five key capitals throughout history, along with other connections inadequately modeled by the gravity approach, to discuss the self-perpetuation effects of social networks. We discern that cities with elevated political stature or a sizable population/economic foundation tend to exhibit a more robust self-reinforcing influence on their social networks.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.000
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 head0.081
GPT teacher head0.388
Teacher spread0.307 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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