The gender gap in voting in post-conflict elections: Evidence from Israel, Mali and Côte d’Ivoire
Bibliographic record
Abstract
In this article, we first formulate some theoretical expectations about the development of the gender gap in voting in post-conflict situations. Second, we test these expectations on five cases, including two civil wars, the Ivorian Civil War (2011) and the Malian Civil War (2013–2015), and three major international Israeli conflicts, the Yom Kippur War (1973) and the First and Second Lebanon Wars (1982–1985 and 2006). We do so by comparing women’s and men’s turnout before and after a conflict using individual voting data and find that the sum of the nine factors we identify (i.e. duration of war, type of warfare, end of fighting after ceasefire/peace settlement, change in workforce participation, international involvement in the peace process, international development aid, the militarization of politics and female social movement activism) explain changes in the gender gap in voting after the conflict in three of the five cases we study.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".