Self-Insert Fanfiction as Digital Technology of the Self
Bibliographic record
Abstract
Self-insert fanfiction is a long-established but still controversial mode of writing, even within the already marginalized genre of fanfiction. Moreover, many of the specific terms and practices used to describe this kind of writing have not been formally explored or theorized. We maintain that self-insert fanfiction can be understood as a digital technology of the self, building upon Foucauldian roots and extending into digital platforms and their affordances. We begin by making connections to the precedents established by “Mary Sue” characters, then continue by tracing the shifts from those conversations to more explicitly self-insert subgenres of the present day. Then, drawing on a survey of self-insert fanfiction conducted across four platforms (Ao3, FF.net, Tumblr, and Wattpad), we explore how such works can be discovered, read, and engaged with, and we offer specific observations about self-insert subgenres, as drawn from a selection of these works. Ultimately, we maintain, self-insert fanfiction expands the possibilities offered by other digital technologies of the self (avatars, blogging, etc.) by attempting to create a self that can be open to any reader who encounters it, although this expansion is not without its own limitations and drawbacks. We conclude by offering potential directions for further work in this area that fall beyond the scope of this initial exploration.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".