Design, Application, and Factor Structure of a Cultural Capital Questionnaire: Predicting Foreign Language Attributions and Achievement
Bibliographic record
Abstract
Culture, as a variable which explains a great part of individual differences, has proved to be effective in defining the factors to which individuals ascribe their success or failure. This study introduced a completely new perspective to the relationship between culture and foreign language attributions by making reference to Bourdieu’s concept of cultural capital. To this aim, a questionnaire for measuring cultural capital was designed, applied, and validated. The Factorability of the intercorrelation matrix was measured by two tests, namely, Kaiser-Meyer-Olkin test of Sampling Adequacy (KMO) and Bartlett’s Test of Sphericity the results of which indicated that the factor model was appropriate (0.65, p < .05). Moreover, the results of Exploratory Factor Analysis (EFA) based on the performance of 476 undergraduate university students yielded a two-factor solution of Textual literacy and Musical literacy. Moreover, the survey explored the relationship between the new factors and learners’ foreign language attributions as measured by the Language Achievement Attribution Scale (LAAS) and the Causal Dimension Scale (CDS-II). Results from Pearson product-moment correlation revealed that the total score for cultural capital was significantly related to learners’ ability, effort, and personal attributions. In order to investigate the role of cultural capital in predicting learners’ foreign language achievement, Multiple Linear Regression Analysis was conducted. Results revealed that musical literacy was the best predictor of the listening and speaking skills, whereas reading, writing, and grammar were mostly predicted by learners’ textual literacy. At the end, statistical results were discussed, and implications for English language teaching were provided.
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 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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".