Bibliographic record
Abstract
Abstract Despite growing awareness, misconceptions about autism (also known as autism spectrum disorder or ASD) persist within science, academia, and popular culture. These misconceptions perpetuate harmful stereotypes that contribute to the ongoing stigma, exclusion, and isolation experienced by autistic individuals. A significant barrier to overcoming these challenges is the underrepresentation of autistic voices across multiple fields and disciplines, making it difficult to effectively challenge and transform prevailing social norms and attitudes about disabilities and neurological differences. While efforts are being made by advocates to improve representation through community-based participatory research and coproduction models, the arts remain a particularly powerful yet underutilized tool for disrupting problematic discourse. Through creative expression, the arts offer a unique avenue to subvert reductionistic pathologies, dehumanizing language, and unfavorable depictions of autism that have been deeply engrained in both academic and cultural discourse. This poem critically engages with widespread stereotypes, using sarcasm and humor to reclaim and reshape existing depictions of autism. By doing so, it aims to empower fellow autistic individuals to challenge these narratives and express their own experiences through whatever creative mediums resonate with them, ultimately offering a more nuanced and representative understanding of autism in the process. Keywords: Arts, neurodiversity, critical autism studies, representation, stereotypes, subversion Author’s Note As an autistic scholar and artist, I rely on a combination of lived experience, community involvement, and research training within this creative piece. A significant source of inspiration comes from the 2SLGBTQIA+ community, particularly their powerful reclamation of terms like “queer” that were once used as weapons of marginalization and exclusion. By embracing and redefining these terms, this community has subverted their original pejorative meanings, transforming them into symbols of identity, pride, and resistance. This act of linguistic reclamation not only disrupts the power dynamics of hate and oppression but also fosters a sense of solidarity. In a similar vein, my work seeks to challenge and overturn the derogatory labels and misconceptions that have been imposed upon autistic individuals, using creative expression as a means to redefine our realities. Historically, academic and societal discourses have often dehumanized autistic people through harmful and reductive descriptions, perpetuating what I refer to in this piece as “myths” about autism. Through this work, I critically deconstruct these demeaning representations, employing a sardonic lens to counter these narratives and expose their absurdity. Incorporating examples from my experiences as an autism and neurodevelopmental researcher, I seek to highlight and dismantle these entrenched misconceptions, including deficit-based models, problematic pathologies, and stigmatizing descriptions of autistic individuals. As someone who experiences echolalia (i.e., the repetition of words, phrases, and sentences) and who frequently incorporates pop culture references when communicating with others, this piece also serves as a homage to the songs, films, and television shows that resonate with me and my special interests. To ensure clarity and accessibility for readers, I have included detailed endnotes that explain both the research references and the various pop culture elements, providing a comprehensive overview of the content and its meaning.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".