Demographic consequences of social movements: local protests delay marriage formation in Ethiopia
Bibliographic record
Abstract
Abstract Despite their significance, life-course dynamics are rarely considered as consequences of social movements. We address this shortcoming by investigating the relationship between protest and marriage formation in Ethiopia. Building on scholarship in social movements and insights from family demography, we argue that exposure to protest delays marriage formation. To test our theoretical arguments, we created an original panel dataset using georeferenced data from the 2016 Ethiopia Demographic and Health Survey. We combined the marriage histories of 4,398 young women with fine-grained measures of exposure to local protests that we compiled from two conflict datasets covering events between 2002 and 2016. Using discrete-time event history analyses, we find that protest delays first-marriage formation. Additional analyses suggest that political uncertainty and disruptions in interethnic marriages cannot explain this effect. Instead, we provide tentative evidence that protest delays marriage formation by preoccupying large segments of the marriageable population, rendering them unavailable for this critical life-course transition. Our findings pave the way for scholarship on the demographic outcomes of protest and contribute to understanding marriage patterns in a country where the timing of marriage has far-reaching social consequences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".