Bodies of Knowledge: Politics of Archive, Disability, and Fandom
Bibliographic record
Abstract
The work of critical theory cannot stop when it leaves the classroom, but must encompass the lived experience of the everyday. This essay combines personal narrative, disability theory, and a discussion of archiving strategies to question the boundaries of disability, injury and impairment. Although fandom has an interesting and constructive relationship with disability, injury, and impairment, this paper does not focus on individual fan-works that feature these topics. This essay is instead an examination of the macro-structure of two different archives: TV Tropes and Archive of Our Own. TV Tropes is an informal encyclopedia of narrative devices that uses community engagement to read narratives in a critical yet accessible way. Employing the macro-structure organization of the database, users frame the linkage of pity and disability in an atypical manner that subverts mainstream ableist assertions. This shows us that the structure of the archive allows for opportunities to resist oppressive ideologies. Rather than subverting official archival methods, Archive of Our Own instead provides space for users to create intersectional spaces through personally generated tags. While these websites are examples of how diverse archival strategies can positively engage with disability narratives, the decision to separate the labels of disability and injury is indicative of tensions around the categorization of the body. Examining how the division can be broken in both theory and fandom creates new, productive models of activism.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.096 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".