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Record W2605316845 · doi:10.1017/s0020743815000963

THE VERNACULAR JOURNEY: RAILWAY TRAVELERS IN EARLY PAHLAVI IRAN, 1925–50

2015· article· en· W2605316845 on OpenAlexaboutno aff
Mikiya Koyagi

Bibliographic record

VenueInternational Journal Middle East Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsModernityMiddle classHistoryEthnic groupSpace (punctuation)Socioeconomic statusIdentity (music)SociologyQuarter (Canadian coin)Gender studiesAestheticsPolitical scienceAnthropologyArchaeologyLawDemographyArtComputer science

Abstract

fetched live from OpenAlex

Abstract Exploring how railway technology was incorporated into the everyday lives of Iranians during the second quarter of the 20th century, this article focuses on spatial discourses and practices around the Iranian railway. The first part investigates Iranian journalists' construction of the railway traveler prototype as the propagator of modernity prior to the completion of the Trans-Iranian Railway in 1938. The second part shows how in the 1940s the railway space became a microcosm of the heterogeneous Iranian nation, and explores how middle-class travelers experienced the railway space. I argue that the railway space, rather than creating a homogeneous experience of railway journeys, was conducive to fragmented experiences among its diverse occupants, who were divided by religion, socioeconomic status, cultural orientation, and ethnicity. The visibility of heterogeneity in the railway space compelled modern middle-class travelers to consolidate their class identity and distinguish themselves from the rest of Iranian society. Wanting to achieve a homogeneously Europeanized Iran, they also felt compelled to travel the country more extensively to create a national community connected through direct interaction.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.153
GPT teacher head0.342
Teacher spread0.189 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations7
Published2015
Admission routes1
Has abstractyes

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