Nuptiality Transition and Marriage Squeeze in Lebanon: Consequences of Sixteen Years of Civil War
Bibliographic record
Abstract
This paper examines nuptiality Tends and patterns in Lebanon using the 1996 Population and Housing Survey (PHS), a representative sample of 64,472 households that provides the largest demographic dataset for the country since the 1932 census. There are five objectives to this study. First, to analyze the proportions of single adults by age and sex, and trends and differentials in the mean age at first marriage (MAFM) for both sexes, at the national and governorate levels. Second, to estimate the singulate mean age at marriage (SMAM) for Lebanon and for its governorates. Third, to address some important methodological concerns about the shortcomings of both the MAFM (obtained from the direct question on age at marriage) and the SMAM (computed indirectly from the proportions single) to measuring average ages at first marriage, and to suggest a new measure based upon the combination of both these estimates. Fourth, to explore whether the war had any significant impact on age at first marriage and the marriage market in Lebanon. It is observed that the proportions of single women of childbearing age doubled between 1970 (shortly before the war) and 1996 (five years after war ended). Mate availability ratios (MARs), defined as the number of single males available per 100 single females in the adult age groups, have declined to 75 at age 25 and 50 at age 30, due to a war effect. Finally, the study attempts to answer the question whether the pursuit of higher education by women may adversely affect their marriage prospects.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".