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Enregistrement W4282842745 · doi:10.3389/fhumd.2022.858229

Assessment of Water-Migration-Gender Interconnections in Ethiopia

2022· article· en· W4282842745 sur OpenAlexaff
Lisa Färber, Nidhi Nagabhatla, Ilse Ruyssen

Notice bibliographique

RevueFrontiers in Human Dynamics · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueClimate Change, Adaptation, Migration
Établissements canadiensMcMaster University
Organismes subventionnairesMaastricht Economic and Social Research Institute on Innovation and Technology, United Nations UniversityUniversiteit Gent
Mots-clésNexus (standard)PopulationFocus groupWater qualityCorporate governanceWater stressGeographyBusinessEnvironmental planningDemographic economicsEnvironmental resource managementEnvironmental scienceEngineeringEcologyEnvironmental healthEconomicsMedicineBiologyMarketing

Résumé

récupéré en direct d'OpenAlex

In recent years, water stress has affected Ethiopian people and communities through shrinking water availability/quantity, poor quality and/or inadequate service provision. Water stress is further exacerbated by the impact of extreme events such as droughts and floods. For people exposed to water crises–whether slow-onset water stress or extreme water-related scenarios-migration often emerges as an adaptation strategy. Yet, knowledge on the interlinkages between water stress and migration pathways remains limited and particularly blind on the gender aspects. This paper contributes to the emerging literature on the nexus between water stress, migration, and gender in settings where large numbers of people and population live in vulnerable conditions and are regularly exposed to water stress. Our analysis in Ethiopia adopts the three-dimensional water-migration framework outlined by the United Nations University in 2020 comprising water quantity, water quality, water extremes. In addition, it has been customized to include a fourth dimension, i.e., water governance. Adapting this framework allowed for an enhanced understanding of the complex interactions between water-related causalities and migration decision making faced by communities and populations, and the gendered differences operating within these settings. We adopted a qualitative research approach to investigate the influence of water stress-related dynamics on migration and gender disparities in Ethiopia with a specific focus on opportunities for migration as an adaptation strategy to deal with water stress. Moreover, our approach highlights how gender groups in the state, especially women and girls, are facilitated or left behind in this pathway. Based on the examination of available information and stakeholders' interactions, we noted that when having the chance to migrate to a more progressive region, women and girls can benefit from other opportunities and options for education and emancipation. While existing policy responses for water governance focus on durable solutions, including the creation of sustainable livelihoods, as well as the improvement of (access to) water, sanitation, and hygiene (WASH) facilities and water infrastructure, they remained restricted on socioeconomic dimensions. Gendered aspects seem to be gaining attention but must be further strengthened in national and regional water management plans and public policies. This agenda would involve representation and consultation with different actors such as civil society and international (aid) organizations to support gender-sensitive investment for water management and for managing the spillover impacts of water crisis, including voluntary migration, and forced displacement. Taking note of selected Sustainable Development Goals (SDGs), particularly SDG 5 (gender equality), SDG 6 (clean water and sanitation), SDG 10 (reduced inequality), SDG 13 (climate action and peace) and SDG 16 (peace, justice, and strong institutions), we have outlined recommendations and strategies while discussing the multiple narratives applying to the water-gender-migration nexus. The key points include a focus on long-term sustainable solutions, boosting stakeholder participation in decision making processes, facilitating cooperation at all political levels, and creating inclusive, gender-sensitive and integrated water frameworks comprising support for regulated migration pathways as an adaptation strategy to water and climate crises.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,478
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,066
Tête enseignante GPT0,339
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2022
Routes d'admission1
Résumé présentoui

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