Bibliographic record
Abstract
Intertext and paratext are central to the creation of fantexts and fan communities. It is essential therefore to explore how intertext and paratext affect the production and consumption of fanfiction, which involves the communication of the reader and author. In this chapter, the authors examine one fanfiction platform as a case study using the Fifty Shades of Grey enterprise as a contextual pivot point in order to understand the impact of digital paratext and intertext as tools constructing the medium of fanfiction. Using computer-mediated discourse analysis, the authors explore textual elements of three fanfiction titles, focusing on the paratext, the reader and author interactions, and the intertext, here the relation of the fanfictions to Fifty Shades of Grey and other texts, including those published on the same fanfiction forum (Fanfiction.net). Findings indicate that fanfiction not only includes paratextual elements for author-reader communication, but also that paratext is integral to the creation of fanfiction. As fanfictions evolve, they themselves become intertextual root-texts from which new fanfiction develops.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".