Cidade sem nome, homem sem face: escolhas e estratégias na adaptação fílmica de O homem duplicado
Bibliographic record
Abstract
When it hit theaters in 2013, Enemy, a Spanish-Canadian film directed by Denis Villeneuve that adapts the novel O homem duplicado (2002), by Portuguese writer José Samarago, radically divided critics and audiences. Enemy’s experimental, dark and psychological character also called into question his own fidelity to Saramago’s source text, who, as is common knowledge, is also used to experimenting in prose. In view of the innovative and bold potentialities of the film and the novel, each in its own territory and with its own artifices, what we propose in this article is a study, from the perspective of Adaptation Theories, on the adaptive strategies employed in the remediation process (BOLTER; GRUSIN, 2000) from Saramago’s novel for the cinematographic language. Therefore, we intend to carry out an immanent reading based on the formal and thematic aspects of the two works: alterations and permanence of narrative and imagery elements; formal, symbolic and thematic ruptures and continuities, in addition to the choices and resources of cinematographic language used by the adapters in Enemy, as well as their (possible) motivations. The scope of our analysis is mainly focused on two unusual elements of the novel that were maintained by Villeneuve during the intersemiotic translation process (PLAZA, 2003): the modern city as an unsettling, suffocating and labyrinthine space that dwarfs individuals and takes away their identity; and the cultural, literary and psychoanalytic archetype (RANK, 2013) of the double.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".