Cultural Encounters: Glimpses of the United States in Late Twentieth-Century Romanian Travel Narratives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".