Normative Tensions in the Popular Representation of Children with Disabilities and Animal-Assisted Therapy
Bibliographic record
Abstract
This article contributes to the critical disability and human-nonhuman animal studies literatures through a discourse analysis of newspaper stories about animal-assisted therapy (AAT) and children with disabilities published in the United States and Canada. The articles in our corpus form a recognizable genre that we call AAT human-nonhuman animal interest stories. We pose two central questions of the genre: (1) how is the therapeutic value of AAT constituted? and (2) what are the effects, in discourse, of associating nonhuman animals and children with disabilities in narratives of therapeutic benefit? We emphasize the normative tensions associated with the representation of children with disabilities and nonhuman animals in news stories about AAT. On one hand, news articles objectify children with disabilities, inscribe their need to be made “normal” and silence their own experiences of AAT. On the other hand, they are written in ways that extend and strengthen the disabled body and self through connections with nonhuman therapy animals. They disrupt sharp species distinctions and present narratives of how interspecies relationships formed through participation in AAT co-constitute the agency of nonhuman therapy animals and children with disabilities. We argue that the normative tensions in the popular representation of AAT present important possibilities for intervening in public discourse about disability and nonhuman animals.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.044 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".