“HERE TO HAVE FUN AND FIGHT ABLEISM”: #AUTISKTOK USER BIOS AS NEUROQUEER MICRO-ACTIVIST PLATFORM AFFORDANCES
Bibliographic record
Abstract
User biography sections on digital social platforms (hereafter described as “user bios” or “bios”) are spaces for account holders to take narrative ownership in communicating their identities to other users and interlocutors. Online platforms, such as social media, are increasingly used as community hubs for disabled groups, and especially for autistic people (Author; Author; Sins Invalid, 2019). We focus on #Autisktok, one of many enclaves for autistic community building and cultural production on TikTok. Through a critical/cultural qualitative thematic analysis of #Autisktok user bios, we assess how the user bio mediates self-advocacy, agency, and autistic-centered knowledges on #Autisktok. To investigate how autistic TikTokers use their profile’s bio section as a space for “restorying” mainstream discourses about autism and agency, we draw upon M. Remi Yergeau’s (2018) work on autism and neuroqueer rhetorics and Arseli Dokumacı’s (2023) theory of micro-activist affordances, extending these frameworks toward the digital. We pose the following research questions: How do autistic youth use the bio section on TikTok to (re)story autism diagnosis? What is the user bio’s role in creating a supportive enclave for other autistic creators, users, and activists on the TikTok platform? Three themes emerged from our analysis: the explicit use of autism in the user bio, autism and intersecting identities, and the bio as a space for asserting agentic autistic selfhood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".