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Record W4312697579 · doi:10.7227/jha.080

Gender Transformation in Humanitarian Response

2022· article· en· W4312697579 on OpenAlexaff
Chikezirim Nwoke, Sofiya Popovych, Mathew Gabriel, Logan Cochrane

Bibliographic record

VenueJournal of Humanitarian Affairs · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsCarleton University
Fundersnot available
KeywordsTransformative learningPsychological interventionHumanitarian crisisContext (archaeology)Political scienceHumanitarian aidInequalityState (computer science)SociologyEconomic growthGender studiesDevelopment economicsPsychologyGeographyDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.294
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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