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Record W2522417154 · doi:10.1080/15248372.2016.1197226

How I Attend—Not How Well Do I Attend: Rethinking Developmental Frameworks of Attention and Cognition in Autism Spectrum Disorder and Typical Development

2016· article· en· W2522417154 on OpenAlexaff
Jacob A. Burack, Natalie Russo, Hanna Kovshoff, Tania Palma Fernandes, Jason Ringo, Oriane Landry, Grace Iarocci

Bibliographic record

VenueJournal of Cognition and Development · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSimon Fraser UniversityMcGill University
Fundersnot available
KeywordsPsychologyAutism spectrum disorderAutismCognitionTypically developingFunction (biology)Cognitive psychologyDevelopmental psychologyCognitive development

Abstract

fetched live from OpenAlex

Evidence from the study of attention among persons with autism spectrum disorder (ASD) and typically developing (TD) children suggests a rethinking of the notion that performance inherently reflects disability, ability, or capacity in favor of a more nuanced story that involves an emphasis on styles and biases that reflect real-world attending. We provide examples from behavioral and physiological research in which performance on attention tasks is not solely a function of abilities, or disabilities, per se but rather is also a function of the ways in which they are implemented. Thus, the study of attention both among persons with ASD and in typical development might best be recast in terms of the question of “how” rather than “how well.”

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.001
Science and technology studies0.0010.023
Scholarly communication0.0050.008
Open science0.0020.006
Research integrity0.0020.003
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 head0.030
GPT teacher head0.268
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations25
Published2016
Admission routes1
Has abstractyes

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