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Record W73287255 · doi:10.1093/pch/11.9.566

The more you read, the more you know

2006· article· en· W73287255 on OpenAlexaffabout
Alyson Shaw, Sarah Shea

Bibliographic record

VenuePaediatrics & Child Health · 2006
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsIzaak Walton Killam Health CentreChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsNeed to knowComputer scienceMedicineComputer security

Abstract

fetched live from OpenAlex

It was a doctor who once said “The more that you read, the more things you will know. The more that you learn, the more places you'll go.” That was, of course, Dr Seuss in his wonderful book I Can Read with My Eyes Shut! (1). Dr Seuss, also known as Theodor Seuss Geisel, was not a paediatrician, but he has had enormous impact on the health of millions of children. He did this by creating literature that was simultaneously wacky, inviting and subliminally educational. Generations of children have been lured into an appreciation of books and reading by works like The Cat in the Hat (2) at the same time that their brains were being exposed to the cadence and rhyme that we now understand is so important for the development of literacy. Unfortunately, the existence of quality children's literature is not enough to ensure the development of literacy in Canada. Forty-two per cent of Canadians over 16 years of age lack the reading proficiency required for full participation in our society. This is a critical health issue, and we need to understand it better. In this special issue of Paediatrics & Child Health, a commentary by Don Jamieson (pages 573–574) sets the stage by exploring the social, educational, economic and health implications of literacy. Articles by Fraser Mustard (pages 571–572) and by Susan Rvachew and Robert Savage (pages 589–593) look at the neurobiology of reading, and the organic, social and environmental elements that affect reading development. The important area of dyslexia, which affects 5% to 10% of children, is reviewed by Linda Siegel (pages 581–587). She offers a conceptual framework that may surprise some readers, as well as information about the important areas of assessment and intervention. Dufresne and Masny (pages 577–580) tackle the complex issue of multiple literacies, obviously very relevant in our multicultural and multilinguistic country. We are also fortunate to have a piece from the inspirational Sheree Fitch (pages 575–576) to remind us of the joy of reading and its crucial role in ‘thrival’.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0100.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.348
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2006
Admission routes2
Has abstractyes

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