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Record W4414137987 · doi:10.15640/jehd.v13p16

An Examination of the Importance of Gender and Sibling Characteristics on Academic Perceptions

2024· article· en· W4414137987 on OpenAlexaboutno aff
Glen Sharpe, Tracey Curwen

Bibliographic record

VenueJournal of Education and Human Development · 2024
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsSiblingPerceptionSocioeconomic statusFeelingAcademic achievementSample (material)

Abstract

fetched live from OpenAlex

The present study attempted to investigate whether age, socioeconomic status (SES), gender, and sibling size were associated with the academic perceptions of children. A total of 735 children aged 10 to 13 were included. The study sample was drawn from The National Longitudinal Study of Children and Youth, Statistics Canada Public Use File, which purports to be a representative sample of Canadian children. Academic perceptions were based on responses to four questions about how they feel about school, their academic goals, and how well they are doing in school. Age, SES, and sibling size and composition were not associated with academic perceptions (p>.05). Gender was associated with academic perceptions with females feeling more positively about school and having higher academic goals (p<.01). Females also reported higher overall academic perceptions compared to males (p<.05). The results of the study suggest that age, SES, and sibling composition are not important factors in understanding academic perceptions of early adolescents.

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)0.006
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.383
Teacher spread0.320 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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