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Record W4403489637 · doi:10.33425/2641-4317.1205

Cognitive Processes and Their Relation to Word Reading, Comprehension, and Math Competence in A Sample of School Children Who Live in Poverty

2024· article· en· W4403489637 on OpenAlexfundno aff
Swagatika Samantaray, Prangya Paramita Priyadarshini Das

Bibliographic record

VenueInternational Journal of Psychiatry Research · 2024
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsCompetence (human resources)PsychologyCognitionReading comprehensionComprehensionDevelopmental psychologyPovertyRelation (database)Sample (material)Reading (process)Mathematics educationLinguisticsSocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The present paper reports an investigation into the structure of the relationship between cognitive processes on the one hand, and Reading and Math competence on the other within a neurocognitive frame-work (Planning, Attention, Simultaneous and Successive processing, PASS). The participants were children in Grades 4 and 5 from a school located in a low-SES region of Odisha, India. All of them were English Language Learners whose mother tongue was Odia. Structural Equation modelling showed that Word decoding was best predicted by a latent factor comprising Planning, and Successive and Simultaneous processes, whereas Comprehension was better explained by Successive and Simultaneous processing. Math competence was best predicted by Simultaneous processing and to a lesser extent by Planning. These results were weakened by the deleterious effects of chronic poverty that included lack of exposure to English language and reading materials. In spite of this limitation, broad relationship between PASS processes on the one hand, and Reading and Math achievement on the other, replicated previous findings as noted in a metaanalysis. That lead us to suggest a reason based on the basic universality of structure and functions of the brain.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.054
GPT teacher head0.392
Teacher spread0.338 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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