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Record W4200169593 · doi:10.29173/connections34

The Reception of Olivia Manning’s The Great Fortune in Romania

2021· article· en· W4200169593 on OpenAlexvenueno aff
Cristina Zimbroianu

Bibliographic record

VenueConnections A Journal of Language Media and Culture · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsnot available
Fundersnot available
KeywordsCensorshipCommunismRomanianPoliticsCriticismGuard (computer science)HistoryWorld War IILawMedia studiesPolitical scienceSociologyEconomic history

Abstract

fetched live from OpenAlex

Manning’s (1908-1980) novel The Great Fortune (1960) is the first Second World War novel of a six-part novel series titled Fortunes of War. Set in Bucharest, Romania, the novel portrays the historical events of the first year of the war (1939-1940) and how these affect Romanian society and the English community. The novel was well-received in England, and in 1987 was adapted to a television serial issued by BBC. In Romania, the response of the critics after the communist regime was rather harsh, accusing Manning of misinterpreting Romanian reality. Moreover, considering that Manning portrays not only the wealth of high society but also the misery and the political conflicts of those times with the fascist Guard in the background, it could be stated that in 1960 when the novel was reviewed by the censorship board, it might not have been positively evaluated. Therefore, this article analyses the reception of The Great Fortune in Romania during and after the Communist regime from a historical perspective focusing on critics and censors’ responses to determine whether censorship influenced the reception of the novel in Romania. To undertake this study the censorship files located at the National Archives in Bucharest, as well as articles guarded in various libraries in Romania, were consulted. Keywords: Manning, Second World War, Romania, Bucharest, censorship, criticism, history, reception studies

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.274
Teacher spread0.260 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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