Resonant relations: eco-lalia, political ec(h)ology and autistic ways of worlding
Bibliographic record
Abstract
Echolalia – the repetition of words and phrases gleaned from one's environment – is often treated as a key behavioural marker of autism. Along with other perceived ‘stereotypies’, it is dismissed by Western biomedical and political discourses as disruptive, ‘meaningless repetition’ and targeted for individual and collective elimination in the context of a global ‘war on autism’. However, as this article shows, echoing is also a crucial element of Autistic ways of worlding. That is, it can be integral to forming and maintaining co-constitutive relations and ethical intimacy with other beings through distinctively resonant political-ec(h)ological relations. At the same time, echoing is a political act that can disrupt interwoven neurotypical (NT), colonial, racial and capitalist rhythms of sociality, communication and space. This insight challenges negative stereotypes about the perceived ‘lack’ or ‘impairment’ of Autistic people in the areas of relationality, intentionality and meaning-making. At the same time, it opens up a wider discussion of how Autistic ways of worlding can contribute to the creation of alternative eco-political futures. To flesh out these arguments, I draw on auto-ethnographic research based on my experience as an Autistic and Dyspraxic global political ecologist. In particular, I share elements of my experimental practice of ‘eco-lalia' – a reclamation of echoing as a form of echo-political praxis, expressed here in the form of poetry. In so doing, I argue that ec(h)olalia and other Autistic ways of worlding can contribute to nurturing robust more-than-human relations, confronting violence and creating solidarities across communities marginalized by dominant global norms of ‘humanity’.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".