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Record W4211121386 · doi:10.1007/s40489-019-00164-z

Considering Methodological Accommodation to the Diversity of ASD: A Realist Synthesis Review of Data Collection Methods for Examining First-Person Experiences

2019· article· en· W4211121386 on OpenAlexafffund
David Nicholas, Jesse Orjasaeter, Lonnie Zwaigenbaum

Bibliographic record

VenueReview Journal of Autism and Developmental Disorders · 2019
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchWomen and Children's Health Research Institute
KeywordsPhotovoiceAutism spectrum disorderPsychologyAutismDiversity (politics)Data collectionQualitative researchRelevance (law)CognitionExpression (computer science)Developmental psychologyCognitive psychologyApplied psychologySociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.543
metaresearch head score (Gemma)0.792
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.457
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5430.792
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0110.011
Bibliometrics0.0180.018
Science and technology studies0.0050.013
Scholarly communication0.0180.013
Open science0.0090.011
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.327
GPT teacher head0.436
Teacher spread0.109 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designSystematic review
DomainMethods
GenreReview

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

Citations27
Published2019
Admission routes2
Has abstractno

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