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Record W4366776611 · doi:10.1002/aur.2928

Anti‐ableist language is fully compatible with high‐quality autism research: Response to <scp>S</scp> inger et al. (2023)

2023· letter· en· W4366776611 on OpenAlexaff
Heini M. Natri, Oluwatobi Abubakare, Kassiane Asasumasu, Abha Basargekar, Flavien Beaud, Monique Botha, Kristen Bottema‐Beutel, Maria Rosa Brea, Lydia X. Z. Brown, Daisy A. Burr, Laurence Cobbaert, Christopher R. Dabbs, Donnie Denome, Shannon Des Roches Rosa, Mary Doherty, Beth Edwards, Chris Edwards, Síle Ekaterin Liszk, Freya Elise, Sue Fletcher‐Watson, Rebecca L. Flower, Stephanie Fuller, Dena Gassner, Morénike Giwa Onaiwu, Judith Good, Aimee Grant, Vicki L. Haddix, Síofra Heraty, Andrew Hundt, Steven K. Kapp, Nathan Keates, Trayle Kulshan, Andrew J. Lampi, Oswin Latimer, Kathy Leadbitter, Jennifer Litton Tidd, Marie Adrienne Robles Manalili, Menelly Martin, Anna Millichamp, Hannah E. Morton, Vishnu KK Nair, Georgia Pavlopoulou, Amy Pearson, Elizabeth Pellicano, Hattie Porter, Rebecca Poulsen, Zoë S. Robertson, Kayla Rodriguez, Anne M. Roux, Mary C. Russell, Jackie Ryan, Noah J. Sasson, Holly Smith Grier, Mark Somerville, Cole Sorensen, Kayden M Stockwell, Tauna Szymanski, Sandy Thompson‐Hodgetts, Martine van Driel, Victoria J. VanUitert, Krysia Emily Waldock, Nick Walker, Courtney Watts, Zachary J. Williams, Richard Woods, Betty Yu, Meghan Zadow, Jordyn Zimmerman, Alyssa Hillary Zisk

Bibliographic record

VenueAutism Research · 2023
Typeletter
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsAutismTerminologyPsychologyHarmMultidisciplinary approachDevelopmental psychologySocial psychologySociologyLinguisticsSocial science

Abstract

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Singer et al. (2023) argue that the current lexical shift within autism research towards more neutral terminology hinders accurate scientific description of the wide range of autistic experiences, particularly within clinical and medical contexts. We disagree with these claims. Semantic guidelines for referring to disability, gender, race, and ethnicity are established components of research dissemination (e.g., Clauss-Ehlers et al., 2019), and are more, not less, scientifically precise. Recommendations informed by self-advocates are widely recognized as vital tools for mitigating societal biases—reflected and reinforced by language choices—that harm marginalized communities, including disabled individuals (Scully, 2008). Examples include the American Psychological Association's bias-free and inclusive language guidelines (APA, 2021) and the National Institute of Mental Health's ​​Stigma and Discrimination Research Toolkit (NIMH, n.d.). The language guidelines for autism research that Singer et al. object to were developed through diverse collaborations of autistic and nonautistic researchers, clinicians, scholars, and caregivers, informed by decades of intersectional, multidisciplinary autistic and disability rights scholarship. Specific recommendations, such as replacing terms like “risk” and “co-morbid” with the accurate equivalents “likelihood” and “co-occurring” offer a more inclusive alternative to the deficit construals that predominate most published autism research (Botha & Cage, 2022). Practical and inclusive terminology enables discussion of heterogeneity in autism presentation, is less likely to reinforce bias, and is more respectful than terminology with negative connotations. Importantly, these suggestions align with guidance from autistic people designated “profoundly autistic” (Whitty, 2020; Zimmerman, 2023) as well as broader autistic preferences (Keating et al., 2022). In contrast, the terms “required” by Singer et al. (e.g., “profound autism,” “severe,” and “challenging behavior”) are limited in their validity, utility, and specificity, are inconsistently defined, and dehumanizing to many autistic people (Kapp, 2023; Pukki et al., 2022). Specific descriptors of cognitive functioning, support needs, or other characteristics of autistic people are more precise and scientifically accurate than ambiguous terms like “profound” and “severe,” which gloss over strengths and the contexts in which impairments can become significantly disabling. Language consistent with existing guidelines can be used to describe differences that are disabling or impairing and their impact. Moreover, Singer et al. mischaracterize the guidelines' more nuanced engagement with difference and disability as describing “a simple ‘difference’.” Singer et al. assert that some autism researchers have been denied funding and have experienced heckling at public presentations due to language choices, citing a handful of tweets out of their original context. We do not condone harassment and would hope that feedback is delivered respectfully and with the intent to educate, not harm. Still, nonautistic people hold nearly all the power in autism research, including funding and publishing decisions. Members of the public have few opportunities to engage with researchers, and open platforms such as social media enable direct engagement between researchers and communities. Autism researchers should expect their work to be consumed and critiqued by the public, including autistic people affected by their research. Such critique is not bullying or weaponization, but feedback that offers autism researchers an opportunity for reflection and dialogue with the wider autistic community they purport​​ to serve. Autism researchers with concerns about how their work will be received should proactively seek guidance and consultation from autistic scholars and self-advocates. Further, Singer et al. claim that language guidelines “should not be dictated by mostly white, non-Hispanic individuals.” This statement reflects an ignorance of the scholarship of autistic people of color who have contributed to language guidance, including those designated as “profoundly autistic” and their families (Malone et al., 2022; McGann, 2021). These contributions have been made despite the significant barriers and discrimination that contribute to the pervasive underrepresentation of marginalized perspectives in the field. This includes autistic researchers (including minimally or nonspeaking), Indigenous and those representing the Global Majority, and others who endure the ongoing and disparate consequences of biased language and research agendas (Jones, 2022; Jones & Mandell, 2020). Tackling this power imbalance and increasing accountability is essential for improving autism research. Lastly, we extend an invitation for everyone to engage with respectful terminology to create more inclusive, representative, scientifically rigorous, and beneficial research for the entirety of the spectrum. This letter is authored and co-signed by a diverse group of autistic researchers, scholars, clinicians, and self-advocates with a wide range of clinical presentation and support needs, as well as nonautistic researchers, scholars, clinicians, and loved ones of autistic people. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0080.016
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0070.007
Research integrity0.0020.026
Insufficient payload (model declined to judge)0.0010.028

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.

Opus teacher head0.183
GPT teacher head0.443
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations35
Published2023
Admission routes1
Has abstractyes

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