Film Adaptation as an Act of Communication: Adopting a Translation-oriented Approach to the Analysis of Adaptation Shifts
Bibliographic record
Abstract
Contemporary theoretical trends in Adaptation Studies and Translation Studies (Aragay 2005; Catrysse 2014; Milton 2009; Venuti 2007) envisage synergies between the two areas that can contribute to the sociocultural and artistic value of adaptations. This suggests the application of theoretical insights derived from Translation Studies to the adaptation of novels for the screen (i.e., film adaptations). It is argued that the process of transposing a novel into a filmic product entails an act of bidirectional communication between the book, the novel and the involved contexts of production and reception. Particular emphasis is placed on the role that context plays in this communication. Context here is taken to include paratextual material pertinent to the adapted text and to the film. Such paratext may lead to fruitful analyses of adaptations and, thus, surpass the myopic criterion of fidelity which has traditionally dominated Adaptation Studies. The analysis uses examples of adaptation shifts (i.e., changes between the source novel and the film adaptation) from the filmP.S. I Love You(LaGravenese 2007), which are examined against interviews of the author, the director and the cast, the film trailer and one film review.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".