School Factors & Cumulative Inequality: A Comparative Study of Elementary Education in Pakistan
Bibliographic record
Abstract
Stratification is deeply rooted in the networks of educational institutes at both macro and micro level. It leads to difference in accumulation of educational capital. Since the role of equal primary education in development cannot be denied; one of the key skills for success is the “linguistic key”; the English language skill. This paper aims to highlight aspects that lead to disparity among educational institutes and explore differences among Socioeconomically Divided School Systems at elementary level. Specifically this work examines trends in school factors to compare how these factors accumulate to a specific kind of linguistic exposure across socioeconomically divided school systems in Pakistan. In this ethnographic study, cumulative inequality theory of advantage and disadvantage is used as a theoretical framework and data is gathered from 30 elementary schools in Rawalpindi through demographics, teacher interviews and test scores. The results of the research show which factors play a significant role in cumulative advantage or disadvantage. The results are significant for policy makers, educationists, teachers and the larger society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".