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Record W2201724838 · doi:10.2495/wrm150281

A framework for assessing capacity in water governance

2015· article· en· W2201724838 on OpenAlexafffundabout
Amber Zary, Henning Bjørnlund, Wenping Xu

Bibliographic record

VenueWIT transactions on ecology and the environment · 2015
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of Lethbridge
FundersAlberta Innovates
KeywordsCorporate governanceBusinessStakeholderCollaborative governanceContext (archaeology)Government (linguistics)Environmental resource managementProcess (computing)Stakeholder engagementResource (disambiguation)Water resourcesIntegrated water resources managementResource management (computing)Environmental planningProcess managementEnvironmental economicsPolitical sciencePublic relationsEnvironmental scienceEconomicsComputer scienceGeographyEcology

Abstract

fetched live from OpenAlex

Alberta's water governance is shaped by a complex web of provincial and federal acts and policies, inter-provincial, inter-territorial, and international management and allocation agreements, and local government bylaws. Adding to the intricacy, water governance is a collaborative process with government departments, government mandated agencies and non-governmental organizations involved. A diverse landscape and varying water needs also contribute to the complexity of water management. Recently, Alberta's water governance has evolved to become more of a collaborative bottom-up rather than top-down approach to management involving a wide range of stakeholders. The direction for water resource management has been driven primarily by the Water for Life strategy. With the shift from a top-down to a bottom-up approach to water management, many questions arise around the capacity of stakeholders to fulfill their roles in this governance structure. In this context, the paper develops a conceptual framework to assess stakeholder capacity in water governance which can be modified to be used in other geographic and resource contexts.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.190
Teacher spread0.170 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2015
Admission routes3
Has abstractyes

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