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Record W2606634362

Watershed Governance for Rural Communities: Aligning Network Structure with Stakeholder Vision

2017· article· en· W2606634362 on OpenAlexaffvenueabout
Darwin Horning, Bernard O. Bauer, Stewart Cohen

Bibliographic record

VenueJournal of rural and community development · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsEnvironment and Climate Change CanadaUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Northern British Columbia
Fundersnot available
KeywordsVisionCorporate governancePanacea (medicine)StakeholderStatus quoPolitical scienceEnvironmental governancePublic administrationEnvironmental planningHumanitiesEnvironmental resource managementBusinessGeographySociologyPublic relationsEconomicsLaw
DOInot available

Abstract

fetched live from OpenAlex

Water governance often adopts one of two end-member frameworks: (a) centralized, command-control structures, or (b) distributed–collaborative networks. The former typifies the traditional style of water governance that has reigned for the past century, whereas the latter is increasingly touted as a panacea to the evolving challenges of water resource management in a time of rapidly changing drivers (e.g., climate change, urbanization). This study applies Social Network Analysis (SNA) to two case-study watersheds in south-central British Columbia in order to assess the (mis)alignment between water governance network structure and stakeholder objectives regarding adaptation to the pressures imposed by climate change. The results indicate that rural, water-scarce regions continue to be burdened by centralized, command-control style structures that reinforce the status quo in watershed governance (Neef, 2009). This reality marginalizes stakeholders at the peripheries of the network, who may represent a silent but significant voice in regard to future visions for watershed governance. The management of common-pool resources in rural areas will likely remain a difficult challenge without social networks that are designed strategically so as to become better aligned with stakeholder visions. Keywords: adaptive; bridging; knowledge transfer; learning; social network ------------------------------------------------------------- Resume La gouvernance des ressources en eau adopte souvent une des deux structures diametralement opposees: (a) centralisee, des structures de commande de controle, ou (b) distribuee - reseaux collaboratifs. La premiere caracterise le style traditionnel de gouvernance d'eau qui a regne pendant le siecle passe, tandis que la derniere est de plus en plus vantee comme la panacee aux difficultes rencontrees dans la gestion des ressources en eau, en periode de facteurs de changements rapides (ex.: changements climatiques, urbanisation). Cette etude applique la methode de l'analyse des reseaux sociaux (ARS) a deux etudes de cas de bassins hydrologiques, dans le centre-sud de la Colombie-Britannique afin d'evaluer l'alignement et le desalignement entre la structure des reseaux de la gouvernance de l'eau et les objectifs des intervenants cles, pour toutes les questions relatives aux pressions imposees par le changement climatique. Les resultats indiquent que les regions rurales, pauvres en eau, continuent d'etre accablees par un style de structures de commande de controle centralise qui renforce le statu quo dans les gouvernances de bassins (Neef, 2009). Cette realite marginalise les intervenants cles a la peripherie du reseau, qui peut representer une voix faible mais cependant significative au regard de la vision future de la gouvernance des bassins. La gestion des ressources collectives dans les zones rurales demeurera probablement un defi difficile sans reseaux sociaux designes strategiquement dans le but d'etre mieux enlignes avec les visions des intervenants.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.247
Teacher spread0.217 · 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 designObservational
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

Citations24
Published2017
Admission routes3
Has abstractyes

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