Digital capital and belonging in universities: quantifying social inequalities in the Philippines
Bibliographic record
Abstract
This study examines social inequalities in Philippine universities that were exacerbated during the COVID-19 pandemic. A quantitative approach using a national sample of 677 university students was utilized to measure the mediating role of digital capital on social inequalities associated with belonging to academic spaces. For the purpose of determining direct and indirect impacts, structural equation modeling (SEM) was employed. Sociodemographic (i.e., gender, age, type of residence, and family income) and educational (i.e., type of university, year in the university, and excellence criterion) characteristics were the direct predictors that were examined as exogenous variables for both digital capital and belonging. Results indicate that type of residence (β=0.200, p<0.05), family income (β=0.220, p <0.001), and excellence criterion (β=0.271, p <0.01) are major determinants of digital capital. The model also shows that belonging is significantly predicted by age (β=0.087, p <0.05), family income (β=-0.207, p <0.001), and digital capital (β=0.576, p <0.001). Lastly, the findings reveal that the impacts of type of residence (β=0.116, p <0.05), family income (β=0.127, p <0.001), and excellence criterion (β=0.156, p <0.001) on belonging are successfully mediated by digital capital. These results suggest that there are indeed differences in students’ abilities to accumulate digital capital and that digital capital enhances the sense of belonging to and together in academic spaces for certain groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".