Bibliographic record
Abstract
Theorists suggest that participatory readers create mainstream-based texts - fan crafts - in order to address the ways they are 'hailed' by the themes and subject positions offered by a text by becoming textual re-writers (Jenkins, 1992,2003 ; Busse & Hellekson, 2007; Chander & Sunder, 2007; Willis, 2007). Re-writers force their personal position or opinions into the centre by creating fanworks based in and on established media texts. The 'Mary Sue' is a self-gratifying fan-crafting trope centered on an idealistic authorrepresentative character, a wish-fulfillment device for the re-writer that bridges the re-writer's reality and that of her favoured fiction. This paper is a comprehensive summarizing of the 'Mary Sue' and its precedents. It asks how they can be deployed as Meta Sues to actively investigate the self or marginalized subjects in media texts. It is accompanied
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".