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Record W4214629602 · doi:10.1515/9781474405157

The Cinematic Bodies of Eastern Europe and Russia

2016· book· en· W4214629602 on OpenAlexaboutno aff
Ewa Mazierska, Matilda Mroz, Elżbieta Ostrowska

Bibliographic record

VenueEdinburgh University Press eBooks · 2016
Typebook
Languageen
FieldSocial Sciences
TopicEuropean history and politics
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceGeographyHistory

Abstract

fetched live from OpenAlex

A critical exploration of the human body in Eastern European and Russian film Bringing together a range of theoretical and critical approaches, this edited collection is the first book to examine representations of the body in Eastern European and Russian cinema after the Second World War. Drawing on the history of the region, as well as Western and Eastern scholarship on the body, the book focuses on three areas: the traumatized body, the body as a site of erotic pleasure, and the relationship between the body and history. Critically dissecting the different ideological and aesthetic ways human bodies are framed, The Cinematic Bodies of Eastern Europe and Russia also demonstrates how bodily discourses oscillate between complicity and subversion, and how they shaped individuals and societies both during and after the period of state socialism. Case studies include: Andrzej Wajda’s War Trilogy Béla Tarr’s Satantango Wiktor Grodecki Ilya Khrzhanovsky’s 4 Györgi Pálfi‘s Taxidermia Czechoslovak New Wave Yugoslav Socialist Realism Contributors: Malgorzata Bugaj, University of Edinburgh and the University of Stirling Helena Goscilo , Ohio State University Nebojša Jovanović, Central European University Hajnal Király , Eötvös Lóránd University Ewa Mazierska , University of Central Lancashire Alexandar Mihailovic, Hofstra University Matilda Mroz, University of Sussex Dorota Ostrowska, Birkbeck College, University of London Elżbieta Ostrowska, University of Alberta Ágnes Pethő, Sapientia Hungarian University of Transylvania David Sorfa, University of Edinburgh Calum Watt, King’s College London Bruce Williams, William Paterson University

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.789
Threshold uncertainty score0.574

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 studies0.0010.002
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 head0.029
GPT teacher head0.231
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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