Regional Refugee Response Plans and Violence Against Women: A Comparative Analysis of the Humanitarian Situations in Ukraine and Venezuela
Bibliographic record
Abstract
Regional Refugee Response Plans (RRPs) have emerged as key protection frameworks in the context of displacement. In line with the UNHCR Refugee Coordination Model (RCM), RRPs involve multi-partner and multi-sector response strategies for priority areas established based on region-specific needs. Across RRPs, violence against women (VAW) constitutes a priority area within operational and funding structures on gender-based violence (GBV). The operational and funding structures on GBV in the response plans for the humanitarian situations in Ukraine and Venezuela reveal important insights for economic mechanisms and impacts of displacement on VAW, especially when examined through feminist economics discourse. To shed light on this, this article analyzes sectoral infrastructures and partnerships as indicative operational structures, and funding streams and funding recipients as indicative funding structures. The analysis focuses on high-risk GBV spaces reflective of prevailing challenges in the implementation of the response plans.HIGHLIGHTSEconomic–political power relations inform discrepancies in global migration governance.Flexibility in responses to humanitarian situations leads to incoherent mechanisms and impacts in addressing VAW.Discrepancies are particularly problematic in high-risk GBV settings, as in the cases of Ukraine and Venezuela.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".