Pirates and poachers: Fan fiction and the conventions of reading and writing
Bibliographic record
Abstract
This article explores what teachers and students can learn about contemporary story‐telling from a study of fan fiction – that is, stories created by readers and viewers out of the canonical material of previously published fictions. Drawing on the example of Pirates of the Caribbean, it investigates ways in which fan fiction writers develop codes and conventions to govern themselves. For example, online litmus tests establish when a writer is self‐indulgently writing ‘Mary Sue’ characters into a story; the self‐styled Protectors of the Plot Continuum patrol the fictional limits of an imagined world to make sure that canonical information is not violated by fan fiction writers. This article makes use of such examples to investigate how quality control in fan fiction is codified, and to explore what teachers can learn from such enterprises about contemporary writing, reading and viewing. It compares these possibilities with issues of online literacy outlined by Henry Jenkins under three headings: the participation gap, the transparency problem, and the ethics challenge.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".