MétaCan
Menu
Back to cohort
Record W2136653279 · doi:10.5465/ambpp.2003.13792387

PUBLIC EMPOWERMENT AND ACTIONS IN AN ENVIRONMENTAL MULTISTAKEHOLDER COLLABORATIVE PROCESS[1].

2003· article· en· W2136653279 on OpenAlexaffabout
Marie‐France Turcotte, Christine Dancause, Éric Gedajlovic

Bibliographic record

VenueAcademy of Management Proceedings · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEmpowermentPopulationNatural resource managementNatural resourceRhetoricAction planEnvironmental planningPolitical sciencePublic relationsPublic administrationEnvironmental resource managementSociologyManagementLawGeography

Abstract

fetched live from OpenAlex

This article examines a community-based Multistakeholder Collaborative Process (MCP) on water management. While public participation around natural resource management is now becoming a standard practice for many institutions and researchers, forms of participation are still highly variable and their outcomes remain dubious. In corporate and management circles, the prevailing rhetoric then made a dramatic shift from economic-environmental conflict to a discourse of reconciliation. The Ville-Marie Intervention Zone Committee is an established MCP in Quebec. Since the inauguration of the program, the Saint Lawrence River has been an area of a social experiment. Its goal is to involve the population as a partner in restoring the ecosystem in an extensive program that was initially called the Saint Lawrence Action Plan. Phase I was designed to address the scientific opinions of experts who had identified some 23 areas of prime concern along the Saint Lawrence. The main success was the elimination of 74% of toxic effluent dumped into the river from manufacturers. The ultimate goal of the Ecological Remediation Action Plans is to involve and mobilize the local population in a consensus decision building process in order to plan remedial, protective and promotional projects for the Saint Lawrence River.

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)0.002
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 score0.491
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.234
GPT teacher head0.399
Teacher spread0.165 · 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

Citations0
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicComplex Systems and Decision MakingFrench-language works237,207