From Novel to Film: A Study of Memory, Illness, and Symbols in All the Bright Places in Light of Eneste’s Ecranisation Theory
Bibliographic record
Abstract
A person's psyche is composed of three distinct yet related components: memory, illness, and symbols. The American teen romance film All the Bright Places (2020b) is based on the Jennifer Niven novel of the same name and is available to stream on Netflix. The term ‘adaptation’ describes how books, stories, and comics are interpreted, reworked, and reimagined for use in movies, music, video games, and webcomics. It has an intertextual and reciprocal structure. The narrative of Theodore Finch’s and Violet Markey’s mental illness and suicidality is the focus of Jenifer Niven's young adult book All the Bright Places (2015a). As it's vital to maintain screentime, dramatic effect, and censorship, sequences are frequently altered and excluded in Adaptation. These changes are often not well received by the audience. It was also widely criticised that Brett Haley's adaptation of the book, All the Bright Places (2020b), failed to do the novel justice. The objective of the investigation is to ascertain the modifications implemented in the film and the rationale behind them. Through the lens of Eneste's Ecranisation theory, this study explores the moments that are included and excluded from the movie All the Bright Places (2020b). The three steps in the Ecranisation theory are reduction, variation, and addition. This paper delves into the memory shared by the characters, the portrayal of their illness and the use of symbolism in both the novel and the film. The researcher identifies that the ecranised contents in the film shift the focus from Finch and Violet’s struggle with suicidality to their love story. This shift portrays the film and the story in a lighter way. It also aids the director in preventing the film from being an example of the Werther effect, the phenomenon of suicide contagion.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| 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".