It Matters to the Viewer: Social Reviews of Books Adapted for Film
Bibliographic record
Abstract
On the social reviewing platform Goodreads, reviewers simultaneously assess both book and film when reviewing books with a film adaptation. Using computational methods, we analyze 151,100 Goodreads book reviews about adapted titles and find that the presence of an adaptation alters how amateur reviewers evaluate a book. Through topic modelling, we find three main types of adaptation reviews: the subjective, the critical, and the nostalgic. On close reading, we find that fidelity criticism, a type of criticism that evaluates the success of an adaptation by comparing differences with its source, is common throughout reviews. The fidelity criticism that we find, however, probes into distinct classes of similarities: similarity in plot, characters, bias, and spirit. These reviews are guided by socially constructed “rules” regarding adaptation, including to “read the book first” and that “the book will be better.” Our findings demonstrate how amateur adaptation reviewers navigate social norms about adaptation, but in practice are guided by their personal experiences and preferences, even if that means breaking adaptation norms.Sur la plateforme d'évaluation sociale Goodreads, les critiques évaluent simultanément le livre et le film lorsqu'ils évaluent des livres ayant fait l'objet d'une adaptation cinématographique. En utilisant des méthodes informatiques, nous analysons 151 100 critiques de livres sur Goodreads concernant des titres adaptés et nous constatons que la présence d'une adaptation modifie la façon dont les critiques amateurs évaluent un livre. Grâce à la modélisation des sujets, nous trouvons trois types principaux de critiques d'adaptation : les subjectives, les critiques et les nostalgiques. En lisant attentivement, nous constatons que la critique de fidélité, un type de critique qui évalue le succès d'une adaptation en comparant les différences avec la source, est commune à toutes les critiques. La critique de fidélité que nous trouvons, cependant, explore des classes distinctes de similitudes : similitude de l'intrigue, des personnages, du parti pris et de l'esprit. Ces critiques sont guidées par des "règles" socialement construites concernant l'adaptation, notamment le fait de "lire le livre d'abord" et que "le livre sera meilleur". Nos résultats démontrent que les critiques d'adaptation amateurs naviguent entre les normes sociales en matière d'adaptation, mais qu'en pratique, ils sont guidés par leurs expériences et préférences personnelles, même si cela signifie qu'ils ne respectent pas les normes en matière d'adaptation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".