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Record W2734573752 · doi:10.1142/s1464333217500090

Towards an Environmental Governance Agenda in Regional Environmental Assessment: A Case Study of the Crown Managers Partnership

2017· article· en· W2734573752 on OpenAlexaff
Ayodele Olagunju, Jill Blakley

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsGeneral partnershipCorporate governanceEnvironmental governanceStrategic environmental assessmentEnvironmental planningPolitical scienceDisciplineCollaborative governanceEnvironmental impact assessmentEnvironmental resource managementPublic administrationPublic relationsBusinessRegional scienceSociologyGeographyEconomics

Abstract

fetched live from OpenAlex

In the last decade, the emphasis of regional environmental assessment (EA) has shifted away from simply project approval towards facilitating environmental governance by accommodating heterogeneous stakeholders and emphasising relationship building across diverse institutions. However, there are very few advanced regional EA cases that may be studied to understand how practice has evolved and the implications for regional environmental governance. This paper characterises and assesses the interactions among the members of the Crown of the Continent Managers Partnership (CMP), whereby individuals with planning, policy-making, and EA roles attempted to implement an adaptive approach to regional cumulative effects assessment. Twelve in-depth, semi-structured interviews with key stakeholders provide data used in the investigation. The analysis demonstrates opportunities for an approach to regional EA that facilitates environmental governance through collective visioning, innovative leadership, learning from failure, and collaborative science and management. Lessons from the CMP are relevant internationally to jurisdictions seeking to implement regional EA via multi-disciplinary, multi-jurisdictional partnerships.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.343
Teacher spread0.310 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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