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Record W3095904145

Deliberative democracy in Canadian watershed governance

2018· article· en· W3095904145 on OpenAlexaffabout
Margot Hurlbert, Evan J. Andrews

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversity of WaterlooUniversity of Regina
Fundersnot available
KeywordsWatershedCorporate governanceDemocracyEnvironmental planningDeliberative democracyPublic administrationPolitical scienceEnvironmental resource managementBusinessGeographyEnvironmental sciencePoliticsComputer scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Bottom-up watershed governance that features citizen engagement in decision-making is touted as a panacea for better social and environmental outcomes. However, there is limited agreement on how exactly this engagement occurs, and how it can be assessed. Water decision-making may result in better social outcomes when decision-making is deliberative and democratic. This article brings together a cross-disciplinary framework to assess deliberative democratic practices in local water councils (LWCs) in the Prairie Provinces, Canada. We apply this framework to assess and compare LWCS, using data from a review of secondary sources and semistructured qualitative interviews with members of LWCs. Our framework was useful for identifying strengths and shortcomings of deliberative democracy within and across LWCs. The strengths of the Manitoba model are its significant mandate and stable tax funding. Alberta’s strengths are in the areas of community representation and significant contested deliberation. Saskatchewan’s strengths are its interconnectedness with other organisations, sectors, and governments. While LWCs have made important contributions to local watershed governance, a consideration and comparison of deliberative democratic practices offers options for policy change strengthening the deliberative democratic practices of LWCs.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.004
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.143
GPT teacher head0.527
Teacher spread0.385 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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