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The self‐concepts and perceived competencies of gifted and non‐gifted students: a meta‐analysis

2010· article· en· W1946100085 on OpenAlexaff
Kristin M. Litster, Jillian Roberts

Bibliographic record

VenueJournal of Research in Special Educational Needs · 2010
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychologyCompetence (human resources)Gifted educationModerationMeta-analysisDevelopmental psychologySelf-conceptMathematics educationSocial psychology

Abstract

fetched live from OpenAlex

The current meta‐analysis compares the self‐concepts and perceived competencies of gifted and non‐gifted students. Using meta‐analytic methods to synthesise the results of 40 studies, we found that gifted students scored significantly higher than non‐gifted students on measures of academic and behavioural perceived competence, as well as global self‐concept. Gifted students scored significantly lower than non‐gifted students on measures of appearance and athletic perceived competence. Significant heterogeneity was found in the extent to which gifted and non‐gifted students' scores differed in the academic and global domains. Moderator variables such as participant grade level, method of gifted designation and publication year accounted for systematic differences in these domains. Gifted students' appearance and athletic perceived competencies may benefit from specific intervention, but their beliefs in other areas remain positive.

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)0.014
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.022
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.478
Teacher spread0.401 · 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 designMeta-analysis
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

Citations102
Published2010
Admission routes1
Has abstractyes

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