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Record W1986843633 · doi:10.4000/nuevomundo.65709

Losing sight in the globalized city: Estorvo/Turbulence (2000) by Ruy Guerra and Ensaio sobre a Cegueira/Blindness (2008) by Fernando Meirelles

2013· article· en· W1986843633 on OpenAlexaboutno aff
Carolin Overhoff Ferreira

Bibliographic record

VenueNuevo mundo mundos nuevos · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Theory and Political Philosophy
Canadian institutionsnot available
Fundersnot available
KeywordsSightBlindnessMythologyCivilizationMetaphorCityscapeHistoryAestheticsSociologyArtHumanitiesLiteratureVisual artsPhilosophyArchaeologyOptometry

Abstract

fetched live from OpenAlex

Two Brazilian co-productions, Turbulence (2000) and Blindness (2008), have used their modes of production as a motive to situate their stories in urban spaces that are in fact assembled cities. Turbulence creates a location by drawing on footage filmed in Havana, Lisbon and Rio de Janeiro. Filming for Blindness took place in Toronto, São Paulo, Osasco and Montevideo. In both cases the cities are at once unnamed and recognizable, transnational and familiar. What is more, they have a deep impact on their characters’ emotions within these spaces. In both cases losing (clear) sight is a metaphor for the inability of coping with society’s inhumanity. The aim of this article is to compare the two films, discuss their adaptation from renowned novels and reveal their differences: while Turbulence is an indisciplinary take on a young man’s vision obfuscated by the repressive paternalistic order he lives in, Blindness is a disciplinary approach towards the loss of sight of an entire civilization. While the first film develops an allegorical and heterogeneous city space that aims to make us perceive a repressive (emotional) cultural landscape associated with the Ibero-American world, the second is rather an altered but conventional take on the ancient myth of Oedipus that does not offer new insights by means of the homogenous portrayal of a globalized Western megalopolis.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.296
Teacher spread0.274 · 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.

Study designTheoretical or conceptual
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
Published2013
Admission routes1
Has abstractyes

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