#ActuallyAutistic: Using Twitter to Construct Individual and Collective Identity Narratives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Employing Critical Autism Studies and Narrative Analysis, this project examines how autistic Twitter users engage in narrative meaning-making through social media. By analyzing the hashtags #ActuallyAutistic and #AskingAutistics this project broadly explores how individuals construct identity when lacking access to positive representations and identity communities. Answering the research question, “How do autistic people construct individual and collective identity narratives through Twitter?,” findings indicate that autistic Twitter users use their social media presence to build virtual learning communities. Common knowledge about autism is often oversimplified and highly medicalized. Therefore, autistics use Twitter to make meaning of their experiences that are not represented within cultural notions of what it means to be autistic. Autistic Twitter users reject medicalized narratives by contesting stereotypes, flipping negative narratives into positive stories, re-inscribing “deficiencies” as beneficial, and resisting rehabilitation and “cure.” Users do important social activist work by building strong autistic communities in ways that counter current negative representation, constructing positive self-affirming individual and community identities and resisting eugenic notions that autistic people are “less valuable.”
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it