Writing a New Text: The Role of Cyberculture in Fanfiction Writers’ Transition to “Legitimate” Publishing
Bibliographic record
Abstract
The debate about the monetization of fanfiction and what that might mean for fan writers and systems of publication has been carried out in the blogosphere and among scholars. The publication of the 50 Shades of Grey series (2011–12), and licensed fanfiction on Amazon.com’s Kindle Worlds, however, makes the question of fanfiction publishing largely irrelevant: the monetization of fanfiction is here, regardless of the naysayers. In this article, we move beyond whether or not fanfiction should be circulated in a gift economy or published for profit to examine, through interviews with authors who have “pulled to publish,” the continuum between fandom and traditional publishing. We find that cybercultural fandom provides important benefits for women writers. Although some of the authors who contributed to this study still define authorship as something other than fanfiction, the competing discursive tension in their responses to questions about authorial legitimacy indicates that although the idea of the author may be bound by extant ideology, the author herself is hardly fixed. The fluidity of the respondents’ thinking around authorship is reminiscent of the opportunities offered by the liminal space of the Internet that nurtured their careers. Indeed, the birth of these “legitimate” authors from online spaces is a physical manifestation of Donna Haraway’s cyborg identity moving from a space it requires to exist initially, because of gendered pressures in the “real world,” to actualization.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".