The Magnitude of Cultural Factors That Affect School Enrolment and Retention in Afghanistan: An Analysis Through Hofstede’s Cultural Model
Bibliographic record
Abstract
This study aims to measure the magnitude of the cultural factors that affect enrolment and retention in both primary and secondary education levels in Afghanistan and advocate cultural transformation or modification in Afghanistan to enhance enrolment and retention in school. The study uses quantitative and particularly qualitative data regarding cultural constraints to school enrolment and retention in Afghanistan from secondary sources like Afghan government publications, private publications, publications from non-government organizations, journal articles, newspaper articles, newsletter articles, web documents, dissertations, published interviews, database articles, and books. It employs Hofstede’s cultural dimensions model to analyze the cultural factors. However, the study found out that on the scale of Geert Hofstede’s cultural dimensions model, Afghan culture was characterized by high degree of power distance, masculinity, i.e., conventional gender role focus, high level of uncertainty avoidance, and long term orientation that were deterrents to education in Afghanistan. Finally, it recommends a culture with low level of power distance, femininity focus, low degree of uncertainty avoidance, and long term orientation to increase school enrolments and retention in Afghanistan.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".