Educational Disparities and Conflict: Evidence from Lebanon
Bibliographic record
Abstract
This article examines the impact of Lebanon's civil war (1975–1991) on disparities in education among the country's main religious sects and across various regions. District of registration is adopted as a proxy for religious affiliation through a novel, detailed classification to assess sectarian differentials by region and regional differentials within each major religious group. Findings show that the civil war helped close the gender gap in education across various sects/regions, presumably because many young men joined militias. However, the education of Muslims still lags behind that of Christians. Intra-sectarian disparities remain very pronounced, especially among Sunni Muslims. The article shows that Lebanon's regional and sectarian inequalities that pre-dated the civil war have been largely maintained. The civil war and its aftermath, however, have led to some shift in the balance of power and to some changes in the ranking of particular sects and regions. Drawing upon the work of Weber and Lenski, the authors argue that sectarian/regional inequalities in education in Lebanon are the product of disparities in economic power and differential access to the state resources among the various regions and sects. They conclude by discussing the future of educational inequalities in Lebanon.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".