Normalizing Disability: Tagging and Disability Identity Construction through Marvel Cinematic Universe Fanfiction
Bibliographic record
Abstract
The exploration of identity is a common practice in fanfiction, and scholarship has consistently investigated this fan practice. Yet, despite the presence of disability and disabled characters in fanfiction, this aspect of identity exploration is only sparsely represented in scholarship. This article explores the intersection of disability studies and fanfiction studies through the lens of labelling and tagging, key elements of both fields. Labelling and classification in disability communities are often associated with medicalization, stereotyping, and erasure of individuality, while tagging in fanfiction provides a communicative framework between authors and readers. These differences in functions of labelling and tagging provide the foundation that enables tagging in fanfiction to function inclusively as a normalizing force, despite the problematic role of labelling in disability communities. Three trends in the ways disability is tagged in fanfiction are explored through a close reading of a selection of fanfiction from the Marvel Cinematic Universe: (1) disability is primarily tagged when it is a significant component of the plot, (2) the disability of canonically disabled characters is primarily tagged when that disability directly influences the plot of the story, and (3) mental disability/illness is significantly more represented than physical disability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".