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Record W3123338612 · doi:10.22329/wyaj.v29i0.4485

Assessing Stakeholder Participation in Sub-Arctic Co-Management: Administrative Rulemaking and Private Agreements

2011· article· en· W3123338612 on OpenAlexfundvenueno aff
Sari Graben

Bibliographic record

VenueWindsor Yearbook of Access to Justice · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaFisheries Joint Management Committee
KeywordsRulemakingAgency (philosophy)NegotiationCorporate governancePolitical scienceStakeholderPublic administrationCitizen journalismDemocracySociologyBusinessHumanitiesManagementPublic relationsLawPoliticsEconomicsSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

This paper argues that participatory governance initiatives like co-management can be made effective through agency rulemaking. Using the Mackenzie Valley Environmental Impact Review Board as a case study, this paper affirms that it is possible for marginalized stakeholders to participate in co-management and alter decision-making. By using its formal authority to generate rules that reflect community perspectives, this board contextualized environmental assessment in community-based perspectives. The study of participation presented here illustrates: 1) that a high level of agency support for community participation in rule-making can lead to rules which reflect community perspectives; and 2) that agency implementation of community perspectives has led to the increased use of stakeholder collaboration through private agreement. Nonetheless, the paper also addresses limitations on the ability to translate social needs into privately negotiated agreements where negotiations depart from highly commoditized terms. Consequently, this paper questions the use of negotiated agreements to meet the goals of stakeholder participation, as conceived by deliberative democratic strands of new governance.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.731

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.166
GPT teacher head0.392
Teacher spread0.226 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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