Social and Cultural Capital: Underlying Factors and Their Relationship with the School Achievement of Iranian University Students
Bibliographic record
Abstract
This study explored the relationship between social and cultural capital and school achievement by developing, administering and validating a 35-statement questionnaire to 403 undergraduate and graduate students majoring in Teaching English as a foreign language and Persian Language and Literature and correlating their extracted factors with the grade point average of their high school diploma. The application of the Principle Axis Factoring to the participants’ responses and rotating the extracted factors revealed ten latent variables, i.e., literacy, parental consultation, family-school interaction, family support, extracurricular activities, family relationship, parent-school encouragement and facility, cultural activities, peer interaction and religious activities. Between the two logically developed subscales comprising the social and cultural capital questionnaire (SCCQ) only the social capital showed significant relationship with the GPA (.19, p <.001). Similarly, among the ten factors, parent-school encouragement and facility (.33), parental consultation (.22), family relationship (.20), and family support (.18), correlated significantly, i.e., p <.001, with the GPA. The implications of the results are discussed within a foreign/first language context and suggestions are made for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".