The Construct Validation of a Questionnaire of Social and Cultural Capital
Bibliographic record
Abstract
The present study was conducted to construct and validate a questionnaire of social and cultural capital in the foreign language context of Iran. To this end, a questionnaire was designed by picking up the most frequently-used indicators of social and cultural capital. The Factorability of the intercorrelation matrix was measured by two tests: Kaiser-Meyer-Olkin test of Sampling Adequacy (KMO) and Bartlett’s Test of Sphericity. The results obtained from the two tests revealed that the factor model was appropriate. To validate the questionnaire, Exploratory Factor Analysis (EFA) was performed. The application of the Principle Component Analysis to the participants’ responses resulted in 14 extracted factors accounting for 69% of the variance. The results obtained from the Scree Test indicated that a five-factor solution might provide a more parsimonious grouping of the items in the questionnaire. The rotated component matrix indicated the variables loaded on each factor so that the researchers came up with the new factors, i.e., social competence, social solidarity, literacy, cultural competence, and extraversion. Finally, statistical results were discussed and suggestions were 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".