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Record W4401933717 · doi:10.1515/cwl-2024-2008

The Road to Modernity: “Railway Texts” of the Meiji and Taishō Eras

2024· article· en· W4401933717 on OpenAlexaff
Jing Wang

Bibliographic record

VenueCowrie · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsDalhousie University
Fundersnot available
KeywordsModernityMeiji periodHistoryAestheticsArtAncient historyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract While the railway played a critical role in contributing to the industrial capitalism of almost every modern nation, it was particularly crucial to Japan’s experience of modernity. With their astonishing speed and power, trains not only altered the physical landscape but also created novel spaces within them, fostering new sensibilities at both the individual and national levels. “Railway texts” – literature featuring representations of trains – can thus demonstrate how rail transport took part in everything from transforming quotidian life to nation building. Touching on selected “railway texts” by Meiji and Taishō era authors Natsume Sōseki, Tayama Katai, Tanizaki Jun’ichirō, and Akutagawa Ryūnosuke, this paper illustrates how trains provided a means to reflect on Japan’s cultural, social, and political contexts, serving as a space for imagining modernity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0080.011
Scholarly communication0.0070.006
Open science0.0000.004
Research integrity0.0010.002
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 head0.017
GPT teacher head0.278
Teacher spread0.261 · 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 designNot applicable
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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