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Record W2406040226 · doi:10.20361/g25c9d

Thomas Loves: A Rhyming Book about Fun, Friendship – and Autism by J. Welton

2016· article· en· W2406040226 on OpenAlexvenueaboutno aff
Sean Borle

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsFriendshipAutismPityPsychologyPsychoanalysisMedia studiesSociologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Welton, Jude. Thomas Loves: A Rhyming Book about Fun, Friendship – and Autism. Philadelphia, PA: Jessica Kingsley Publications, 2015. Print.This picture book introduces the day-to-day life of a happy kid, who happens to be autistic. The book starts out presenting Thomas like any other boy, playing with a train. Slowly we learn that he likes to repeat strange sounding words, can't stand loud noises, has a limited diet, flaps his hands if stressed, and requires a picture-plan of what is going to happen each day. The author does not try to make you feel sorry for Thomas or pity him. It is just an introduction to this particular boy. The book is aimed at pre-school children, and both the cartoon pictures by Jane Telford and the rhymed text by Jude Welton will make the book attractive to small children. It would be a good book to use with children who are in a classroom with an autistic child. At the end of the book there are author notes that provide facts about autism. I highly recommend this book for elementary schools, day cares and public libraries.Highly Recommended: 4 stars out of 4Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0520.031

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes2
Has abstractyes

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