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Record W4386626173 · doi:10.1080/2040350x.2023.2255456

Between the Borders of Life and Art: Roman Polanski’s Transgressive Negotiations

2023· article· en· W4386626173 on OpenAlexaff
Sony Jalarajan Raj, Adith K. Suresh

Bibliographic record

VenueStudies in Eastern European Cinema · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsMacEwan University
Fundersnot available
KeywordsSubversionNarrativeAestheticsLiteratureAbsurdityTransgressiveSociologyArtPsychoanalysisPsychologyPoliticsLawPolitical science

Abstract

fetched live from OpenAlex

Roman Polanski’s films are noted for their subversive psychological style that explores themes of sexuality, desire, alienation, and violence. His narratives often reflect a dark sense of humour through which the director perceives the absurdity of the human condition in relation to his own cultural dislocations and artistic eccentricity. This article investigates how different connotations of transgression play a major role in defining Roman Polanski as a filmmaker. It specifically explores how the polysemy of transgression structures Polanski as an artist whose real and cinematic negotiations are often intertwined. Through the constant subversion of moral, cultural, and social discourses, his visual style and narrative ideology maintain a notorious affinity that disturbs the notion of reality and manipulates it with new narrative texts. It is the idea of transgression that changes the way Polanski’s auteur status is perceived, appreciated, and rejected for his actions and creations in the past and their repercussions in the present. Polanski’s works use historical, social, and personal realities to renegotiate his transgressive image in real life by incorporating his contested victim status and persecuted selfhood in narratives that manipulate both the past and present.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.371

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 communication0.0000.000
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 head0.091
GPT teacher head0.307
Teacher spread0.216 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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