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Record W1571081310 · doi:10.4102/sajce.v4i1.66

Bridging the Gap between Advantaged and Disadvantaged Children:

2014· article· en· W1571081310 on OpenAlexaff
Caroline Fitzpatrick

Bibliographic record

VenueSouth African Journal of Childhood Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsDisadvantagedSocioeconomic statusPsychological interventionAcademic achievementPsychologyDevelopmental psychologyExecutive functionsExecutive summaryLife chancesCognitionSocial classEconomic growthMedicinePolitical sciencePopulation

Abstract

fetched live from OpenAlex

Reducing the economic and social burden associated with poor academic achievement represents an urgent social concern. Increasingly research suggests that child characteristics in kindergarten play an important role in charting courses towards academic success. Although math and reading skill are important predictors of later achievement, executive function skills which underlie children’s ability to focus attention and become autonomous, self-directed learners are also likely to play a key role in later adjustment to school. Disadvantaged children perform more poorly on tests of achievement and executive functions. Furthermore, executive functions have been found to partially account for the relationship between socioeconomic status and later achievement. It is possible to target executive functions in at-risk children using specific interventions. Not only are these interventions effective, they are also cost effective. It is proposed that increasing efforts towards promoting executive functions in preschool-aged children represents a promising strategy for reducing economically-based disparities in the education and eventual life chances of individuals.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.664

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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