Exploring the Relationship between Social Capitals and English Language Achievement within a Specific Grade and Context
Bibliographic record
Abstract
An achievement test based on schema theory (S-Test) was developed on the passages comprising the English textbook taught at grade three in state high schools in Iran and administered concurrently with a validated and reliable Social Capital Scale (SCS) to four hundred seventy seven male and female participants. The Z-scores obtained on the S-Test were utilized to divide the participants into high, middle and low achievers. Among the ten factors underlying the SCS, i.e., Self Volunteering, Receptive Relatives, Maternal Supervision, Parental Monitoring, Teacher Consultation, Parental Expectation, Parental Rapport, Family Religiosity, Helpful Others and Parent Availability, high achievers’ Family Religiosity correlated significantly with their S-Test whereas a significant correlation was found between Parental Monitoring and S-Test for middle achievers, indicating that social capitals of these two ability groups function differently. The results also showed while middle achievers’ scores on the semantic subtest of S-Test related significantly and positively to Parental Monitoring, Teacher Consultation and Family Religiosity, they correlated significantly but negatively with Parental Expectation and Helpful Others on the syntactic subtest. The semantic subtest also revealed the highest significant and negative relationship with low achievers’ Teacher Consultation. The findings are discussed 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".