Fifty Shades of Twilight: A Computational Approach to Textual Adaptation
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.We analyse the relationship between the Twilight Saga and 50 Shades of Grey trilogy as a case study on the relationship between published fiction and fanfiction. We assess how this relationship can be studied using tools from computational literary studies (CLS). We examine the processes of theorization, conceptualization, implementation and validation that underly CLS-research. We provide an overview of relevant scholarship from fan- and adaptation studies, then theorize that the adaptation of Twilight into the fanfiction Master of the Universe, which resulted in Fifty Shades, resides primarily in its changing characterization of the protagonists, including their storyworld, and in an increased focus on sex. We then conceptualize these aspects as revolving around the words used to describe characters, and the frequency with which sexually explicit words occur. We implement our approach by detecting words related to characterization and sex using three methods: corpus analysis using Sketch Engine and LIWC, and the measure of pointwise mutual information. In the end, although anecdotal evidence points to patterns of adaptation across our two corpora, we find that these methods cannot be validated. The impossibility of validating our methods points to a divide in CLS-research: on the one hand, CLS can be understood as a way of testing empirical claims about literature using computational methods. In this view, theorization, or a researcher’s subjective reading or analysis is always the first step in the research design, and one may find, as we do here, that not all empirical claims about literature can be captured in computational operationalizations yet. However, CLS can also be understood as an attempt to employ computers so new questions can be asked of literature. To answer the kinds of questions we started with – questions inspired by human intuitions about literature – we need tools better attuned to the subtleties of narrative text.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".