Women in Times of Crisis: Rethinking the Extraordinary and the Everyday
Bibliographic record
Abstract
This literature review was conducted in preparation for a conference with the same name, held online on 18 October 2024. The conference brought together researchers and staff members from Columbia University, Sciences Po Paris and the Université Paris 1 Panthéon-Sorbonne. The literature review examines crises (economic and political crises as well as states of war in particular) going back to the 1990s, which have sometimes been described as a "holiday from history" in "the West", following the fall of the Berlin Wall (1989) and the peaceful breakup and transition of the Soviet Union and Eastern Europe to more market-based regimes and varying degrees of political pluralism. It should however not be forgotten that the 1990s also witnessed the Rwanda genocide, the wars in Yugoslavia and the start of the Great War of Africa (1998-2003). The literature review pursues by examining the succession of crises in the 21st century, including the global financial crisis (2007-2009) and the Covid pandemic (2020-2021), along with more “persistent states of crisis” related to climate change, migration and national populism. By carrying out a series of case studies, the literature review strives to analyse how crises specifically impact women, be it through economic disadvantage, poorer or inappropriate access to support and public services, and, above all, the increased violence women experience during crises (ranging from intimate partner violence to systematic rape as a tool for breaking the personalities of victims and the cohesion of social groups). The literature review was disseminated prior to the conference to support participants' subsequent work.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".