‘Does anybody have that fic…?’: The fannish reclamation of the circulating library
Bibliographic record
Abstract
Associated with frivolous reading and moral repugnance, eighteenth-century circulating libraries provided women and members of the working class easy access to novels. Almost three centuries later, fans who create and own private, file-based collections of fanfiction have reclaimed the circulating library structure. Now used to preserve the very kinds of content Victorian detractors were so against by the communities they feared would be corrupted, transformative fans (mostly women and queer folx) share copies of works from their personal collections to interested readers. These serve the dual function of archiving fic for pleasure on the part of the collector, as well as storing a stable format of the work – one that is less likely to be made obsolete. Because fans do not expect these files to be returned, a private fic collection is therefore not a library at all, but an archive, one that is dependent on individual taste but connected to the community through a network of endless copying, gifting and regifting. Therefore, studying these fic collections not only gives us insight into fannish reading habits over time but also points to strategies of archiving and cultural preservation in the face of technological debt.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".