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Record W2931375168 · doi:10.3390/w11040663

Governance Arrangements for Integrated Water Resources Management in Ontario, Canada, and Oregon, USA: Evolution and Lessons

2019· article· en· W2931375168 on OpenAlexaffabout
Nigel Watson, Dan Shrubsole, Bruce Mitchell

Bibliographic record

VenueWater · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUniversity of WaterlooWestern University
FundersOregon Watershed Enhancement Board
KeywordsIntegrated water resources managementCorporate governanceScope (computer science)Context (archaeology)Political scienceReflexivityScale (ratio)MistakeEnvironmental resource managementPublic administrationEnvironmental planningBusinessWater resourcesSociologyGeographyEconomicsEcologySocial scienceLaw

Abstract

fetched live from OpenAlex

Guidelines produced by some major international organisations create a misleading impression that Integrated Water Resources Management (IWRM) can be implemented in a standardized fashion. However, contextual conditions vary from place to place, and differences in beliefs, attitudes, customs, and norms sensibly influence interpretation and implementation. Experiences with IWRM in Oregon (USA) and Ontario (Canada) are examined with regard to scope, scale, responsibility, engagement, finances and financing, and review processes and mechanisms. Development of IWRM and the evolution of governance have been shaped by different concerns and beliefs. Oregon has adopted a locally-driven and entrepreneurial approach, whereas Ontario developed a co-operative inter-governmental approach. In both cases, IWRM governance has also evolved due to changes in funding and priorities, which have benefitted some catchments and communities more than others. Both cases provide positive examples of reflexivity and resilience, and demonstrate the importance of review processes and strong cross-scale connections for effective governance. While underlying principles may be relevant for other locations, it would be a mistake to think that either of the two approaches for IWRM could be replicated elsewhere in their exact form. Implementation of IWRM in other parts of those countries and the world should, therefore, start with careful analysis of the local context, and existing governance arrangements and governmentalities.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.577

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 head0.006
GPT teacher head0.167
Teacher spread0.161 · 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 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

Citations21
Published2019
Admission routes2
Has abstractyes

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