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Enregistrement W3167466560 · doi:10.1002/wwp2.12057

Knowledge building and community learning for a more sustainable future

2021· article· en· W3167466560 sur OpenAlexaboutno aff
Susana Neto, Jeff Camkin

Notice bibliographique

RevueWorld Water Policy · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueWater Governance and Infrastructure
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSustainabilityLivelihoodEquity (law)AgriculturePolitical scienceWater scarcityClimate changeHarmony (color)Economic growthEnvironmental resource managementEnvironmental planningPublic relationsDevelopment economicsSociologyGeographyEconomicsEcologyLaw

Résumé

récupéré en direct d'OpenAlex

Welcome to the first issue of World Water Policy Journal for 2021! Today, Earth Day 2021, there is no shortage of messages about the need for us to live more sustainably. From US President Joe Biden's welcome commitment to slash US greenhouse gas emissions by 50% by 2030, to the many thousands of online and local events around the world, the message is clear—our lives and our livelihoods depend upon us doing a better job of protecting this planet. So, it is time that we all ask ourselves the same question: what is the legacy we want to leave to our children and future generations? Sustainability is better seen as a measure of the relationship between the community as learners and their environments, rather than an externally designed goal to be achieved (Sriskandarajah et al., 1991). In other words, sustainability is about all of us learning to live in better harmony with the environment around us. This issue of World Water Policy aims to reflect that central idea, bringing to readers papers ranging from global issues such as climate change and gender equity, to drivers of community water quality and attitudes to water saving on the farm. The first paper is from Ahmad Zeeshan Bhatti and co-authors who analyze historical precipitation and temperature data for Prince Edward Island, Canada and discuss the climate change impacts on precipitation and temperature. Noting some emerging trends, the authors highlight the importance of considering those trends in water policy and adaptation strategies for rainfed agriculture. Gender inequity in urban water governance is the theme of the second paper by Basundhara Bhattarai and co-authors. Focusing on the example of two towns in Nepal, the authors find that tackling gender inequity in water management requires a transformative approach that seriously considers women's voices, critical awareness, and open deliberation over the causes and consequences of the current approaches and practices. In the first of three contributions from Africa in this issue, Lucky Onyeche reflects on the role of water in the local culture and examines trends in access to drinking water for the people of the Etche Ethnic Nationality of the Niger Delta, Nigeria. In his discussion paper, the author blends baseline surveys, and interviews of members of two communities and his own lived experience as both a community member and a Technical Assistant responsible for developing proposals for new water sources. In doing so, Lucky provides captivating insights into the practical challenges and opportunities for achieving SDG 6, to ensure safe drinking water and sanitation for all. First released in 1987 and updated in 2002 and 2012, India's National Water Policy (NWP) aims to provide a broad framework of guidelines for all Indian states so that planning and the development and optimum utilization of water resources can be achieved. The research paper by Fakeha Parween and Ajai Singh presents a review and analysis of implementation of the NWP by the eastern states of India. Concluding that many of the major recommendations of the NWP are yet to be implemented, the authors provide a suite of practical suggestions for moving forward. In the next paper, George Kiambuthi Wainaina presents his research into the adoption challenges for drip irrigation technology in Kenya. Blending a desktop review and key informant interviews, the author systematically analyses the barriers to adoption of drip irrigation in the Kenyan context to identify potential pitfalls in both policy and practice. Finding a clear gap in the context-specific literature, George calls for more research on important multidisciplinary areas, and for actors to publish their stories of both successes and failures. The influence of dams and barrages on water quality and phytoplankton diversity in the upper Ganga basin is the focus of the contribution by D.S. Malik and co-authors. Noting the vital role that water quality plays in freshwater biodiversity, the authors show that human activities have negatively impacted on water quality and biodiversity in the upper Ganga basin and highlight the importance of monitoring and regulating such impacts through policy and practice. In the final research paper of this issue, Joan Abla Ketadzo, Nsalambi V. Nkongolo, and Mark McCarthy Akrofi examine the quality of groundwater that forms the main water source for five major slums in Accra, Ghana. Noting that overall water quality was poor, and did not meet WHO standards, the authors identify the causes of groundwater pollution and lay out clear recommendations for how the situation can be improved. In this issue, we also announce a new section—the Water Policy Lab. We have invited Hemant Ojha, Basant Maheshwari, and Basundhara Bhattarai to write a Guest Editorial and introduce the Water Policy Lab approach, which aims to initiating dialogue among those who are concerned with two key challenges: water insecurity, and the disconnect between water knowledge and its application in policy and practice. This new section will be a regular feature in World Water Policy. We trust that you will enjoy this issue of World Water Policy journal and look forward to your contributions to the next issue, to be published later in 2021.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,921
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0020,000
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 Opus0,015
Tête enseignante GPT0,324
Écart entre enseignants0,309 · 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.

Devis d'étudeSans objet
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

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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