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Record W1980410577 · doi:10.5539/ijel.v1n2p50

Parental Education and Social and Cultural Capital in Academic Achievement

2011· article· en· W1980410577 on OpenAlexvenueno aff
Reza Pishghadam, Reza Zabihi

Bibliographic record

VenueInternational Journal of English Linguistics · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAcademic achievementCultural capitalSocial capitalLiteracyRegression analysisCompetence (human resources)Context (archaeology)Social psychologyDevelopmental psychologyMathematics educationSociologyPedagogySocial scienceGeographyStatistics

Abstract

fetched live from OpenAlex

The relationship between social and cultural capital and academic achievement was explored in this study by administering the Social and Cultural Capital Questionnaire (SCCQ) to 320 undergraduate students majoring in English language, and correlating the respective subscales with the learners’ university GPA. All five factors of SCCQ were found to be correlated significantly with the learners’ GPA. Moreover, having conducted the regression analysis, the researchers found out that literacy and cultural competence were predictive of higher GPA. The researchers then entered parents’ educational levels into the regression model. The results of this analysis indicated that, together with literacy, mother’s educational level predicted 23% of the variances in learners’ GPA. However, father’s educational level was not a good predictor of academic achievement. The implications of the results were discussed within a foreign language context and suggestions were made for future research.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.042
GPT teacher head0.348
Teacher spread0.306 · 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

Citations69
Published2011
Admission routes1
Has abstractyes

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