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Record W2794608733

Keynote: Is it a Pet or Not? Students Claiming Disability Assistance Animal Status for their mental or emotional health

2017· article· en· W2794608733 on OpenAlexaboutno aff
Paul Harpur

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietyAnimal welfarePsychologyMental healthCertificationResource (disambiguation)Animal-assisted therapyGovernment (linguistics)Service (business)Medical educationApplied psychologyPublic relationsMedicinePolitical sciencePet therapyBusinessPsychiatryComputer scienceMarketingLaw
DOInot available

Abstract

fetched live from OpenAlex

The definition of disability assistance animal in anti-discrimination laws is being interpreted more broadly than ever, as therapists and scientists identify new ways animals can assist people with impairments. Use of non-canine species and uncertainty with training guidelines are creating resource and evidential challenges for stakeholders who need to determine how to train an animal, if the animal should be certified (government), and if the animal should be granted access (university staff and students). When the disability is obvious and the animal is a traditionally accepted service dog, then the assessment is easy. For example, a blind person with a Labrador in a distinctive harness. Other situations are more complex, for example, a lady with depression with a cat; a man with anxiety and a duck; a student managing stress and their snake. This leads to problems such as who assesses the animal and handler to determine if they qualify for protected status; what criteria do they use; how do they communicate the outcome of that assessment in a way that balances privacy, but allows effective access and appropriate denials of access.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.076
GPT teacher head0.424
Teacher spread0.348 · 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; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes1
Has abstractyes

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