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Record W2115125453 · doi:10.1177/0022219411428807

Perceptions of Academic Performance

2011· article· en· W2115125453 on OpenAlexaff
Nancy L. Heath, Elizabeth Roberts, Jessica R. Toste

Bibliographic record

VenueJournal of Learning Disabilities · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsMcGill University
Fundersnot available
KeywordsSpellingPsychologyPerceptionLearning disabilityDevelopmental psychologyIllusionAcademic achievementSample (material)Cognitive psychology

Abstract

fetched live from OpenAlex

Children with academic and behavioral difficulties have been found to report overly positive self-perceptions of performance in their areas of specific deficit. Researchers typically investigate self-perceptions in reference to both actual performance and ratings by teachers, peers, and parents. However, few studies have investigated whether or not adolescents with difficulty report overly positive self-perceptions. The present study sought to investigate self-perceptions of performance in the domains of spelling and math among a sample of adolescents with and without learning disabilities (LD). A total of 58 adolescents with and without LD participated. Adolescents with LD significantly overestimated their performance in math relative to their actual performance, but not in spelling, reflecting the predominant difficulty of the sample in the area of math rather than spelling. In addition, the magnitude of the gap between math predictions and actual performance was significantly greater for the group with LD than the group without LD. Findings support the existence of positive illusions in specific areas of deficit.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.316
Teacher spread0.259 · 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 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

Citations19
Published2011
Admission routes1
Has abstractyes

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