Gender Transformation in Humanitarian Response
Bibliographic record
Abstract
Within bilateral and multilateral funding circles, there has been a strong and growing emphasis on the importance of understanding and responding to gender inequalities in humanitarian settings. However, given the often-short funding cycles, among other operational challenges, there is limited scope to incorporate interventions that address the root causes and social norms underpinning gender inequalities, or other gender transformative interventions. In the context of the decade-long crisis in the Lake Chad Basin, fuelled by incursions from non-state armed groups (NSAGs), including Boko Haram, and the resultant protracted and chronic humanitarian crisis, this article examines Save the Children’s child nutrition programmes in northeast Nigeria. Taking an ethnographic approach focused on volunteer-driven peer support groups (mother-to-mother and father-to-father) that aim to increase knowledge on best practices for infant and child nutrition, we investigate whether these activities are transforming societal gender norms. While evidence shows an improved understanding and awareness of gender-positive roles by both men and women, restrictive gender norms remain prevalent, including among lead volunteers. We suggest the possibility of longer term shifts in power dynamics in the home and society at large as well as suggest how humanitarian response can better integrate gender transformative programming.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".