MétaCan
Menu
Back to cohort
Record W1586678474 · doi:10.7202/1005807ar

From City of God to City of Men: The Representation of Violence in Brazilian Cinema and Television

2011· article· en· W1586678474 on OpenAlexvenueno aff
Gabriela Borges

Bibliographic record

VenueCinémas Revue d études cinématographiques · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicCultural, Media, and Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterDramaRepresentation (politics)Context (archaeology)NarrativePovertySociologyMedia studiesFilm industryAestheticsHistoryArtVisual artsLiteratureArt historyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

This paper discusses the representation of violence in Brazilian cinema and television through analysis of the TV series City of Men (2003-7), which is a follow-up to the film City of God (2002), with the same actors, sets and non-linear narrative. The project began with the production of the TV episode Palace II (2000), which was developed into City of God ’s script. After receiving international acclaim, it resulted in the production of City of Men . In this context, it is important to emphasise the relationship between cinema and television and their particular features as products of the Brazilian audiovisual industry’s renaissance in the 1990s. The representation of violence is analysed not only as a thematic issue common to Brazilian favelas but also as an aesthetic element of TV drama. The representation of the oppressed has been well known in Brazilian cinema since Glauber Rocha’s manifesto “Aesthetics of Hunger” (1965), in which he argues that films need to be aggressive in order to truly expose poverty. The main point to be addressed, however, is whether the representation of violence in this series conveys, criticises or reflects about what is really happening in Brazilian favelas or if it merely offers an aesthetic look into poverty for the delight of audiences in Brazil and abroad.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.064
GPT teacher head0.265
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 designObservational
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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCinémas Revue d études cinématographiquesSame topicCultural, Media, and Literary StudiesFrench-language works237,207