Losing sight in the globalized city: Estorvo/Turbulence (2000) by Ruy Guerra and Ensaio sobre a Cegueira/Blindness (2008) by Fernando Meirelles
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".