The politics of the digital transition: lessons from slash fan fiction communities at the turn of the millennium
Bibliographic record
Abstract
To understand the relationship between gender and the internet in the present, we must return to the transition from print to digital media at the beginning of the enormous social, legal, and economic changes that we are still living within today. Slash fan fiction communities, that is international communities of primarily women organized around the circulation of amateur stories that set existing characters in new same-sex relationships, faced challenges in carving out protected pockets of digital space wherein women’s relationships, creativity, and erotic imaginations could thrive. They were early adopters and adapters of digital technologies, connecting a web of corporate and independent internet infrastructure and tactics to hide slash from the scrutiny of censors yet also make it searchable and findable by potential new readers. These women’s social and narrative experiments on the early internet offer a unique example of stridently independent tech-savvy foremothers whose labor to create livable spaces within the early web are easily made invisible by corporate and male-focused internet histories. The women of slash fan fiction communities serve as a critical reminder of the economically and sexually radical paths offered by the early web and the possibilities for autonomy and independence that may still remain today.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.022 | 0.036 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".