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Record W2997133754 · doi:10.2478/ewcp-2019-0005

Cultural Encounters: Glimpses of the United States in Late Twentieth-Century Romanian Travel Narratives

2019· article· en· W2997133754 on OpenAlexaboutno aff
Anca-Luminiţa Iancu

Bibliographic record

VenueEast-West Cultural Passage · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTravel Writing and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsRomanianNarrativeCommunismDemocracyHistoryEconomic historyPolitical scienceGeographyEconomyMedia studiesSociologyPoliticsLawArtLiterature

Abstract

fetched live from OpenAlex

Abstract Travel narratives are complex accounts that include a significant layer of factual information – related to the geography, history, and/or the culture of a particular place or country – and a more personal layer, comprising the author’s unique perceptions and rendering of the travel experience. In the last thirty years of transition from a communist to a democratic society, the Romanians have been free to travel to any country they choose; however, during the communist period, especially during the 1980s, travelling to Western, capitalist countries, such as France, Great Britain, Canada, or the United States, was rather limited and fraught with complex issues. Still, Romanian travelers during that time managed to visit the United States, on diplomatic- or business-related exchanges, and published interesting travel stories of their experiences there. Therefore, this essay sets out to capture, from a comparative perspective, the impressions and encounters depicted by Radu Enescu in Between Two Oceans (1986), Ion Dinu in Traveler through America (1991) and Viorel Sălăgean in Hello America! (1992), with a view to analyzing how their descriptions and perceptions of two major urban spaces, New York City and San Francisco, reflect the complexity of the American social and cultural landscape in the late 1970s and mid-1980s.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.226
Teacher spread0.208 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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