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Record W2727603623

Environmental and resource conflicts and conflict resolution practices in coastal areas of the North American Great Lakes: towards an integrated approach for policymaking

2013· article· en· W2727603623 on OpenAlexfundaboutno aff
Olga Skarlato

Bibliographic record

VenueMspace (University of Manitoba) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
FundersParks CanadaPennsylvania Game CommissionMichigan Department of Natural ResourcesU.S. Geological SurveyMinistry of Natural ResourcesPennsylvania Department of Conservation and Natural ResourcesNational Oceanic and Atmospheric AdministrationIllinois Department of Natural ResourcesAustralian GovernmentNew York State Department of Environmental ConservationGreat Lakes Fishery CommissionU.S. Fish and Wildlife ServiceU.S. Department of EnergyU.S. Army Corps of EngineersU.S. Department of CommerceU.S. Environmental Protection AgencyU.S. Department of Agriculture
KeywordsConflict resolutionResource (disambiguation)Environmental resource managementGeographyPolitical scienceEnvironmental ethicsEnvironmental planningRegional scienceEnvironmental scienceComputer scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Environmental conflicts are multi-dimensional. Individual components of environmental and resource-related conflicts are closely interlinked with other structural societal elements, including economic, social, political and cultural developments. Coastal areas are significant for people’s subsistence, as well as industrial development, cultural heritage, and waterways; therefore, they require integrated research approaches and the implementation of comprehensive strategies of resource management, dispute resolution and conflict prevention. This qualitative exploratory study contributes to the development of the field of environmental conflict resolution (ECR) by examining the perceptions and experiences of 52 key stakeholders from the coastal areas of the Great Lakes region of Canada and the United States (US) with regards to environmental and resource conflicts and conflict resolution approaches. The study invited coastal stakeholders such as environmental policymakers, researchers, academics, educators and NGO members to share their perceptions, images, experiences and knowledge about environmental and resource conflicts and conflict resolution practices in the coastal areas of the Great Lakes. The framework of this holistic study integrates public policy, alternative dispute resolution, conflict analysis, project evaluation, dialogue and public participation, education and other creative interventions into an inclusive strategy of integrated environmental and resource management of coastal areas. Analysis of the study participants’ responses revealed several key findings. First, the multi-dimensional character of environmental and resource conflicts and the wide range of coastal stakeholders involved necessitate creating spaces for dialogue and communication among coastal stakeholders, which may facilitate relationship building and encourage collaborative problem solving and constructive conflict resolution. Second, establishing links between science and policymaking within environmental and resource management, as well as introducing conflict resolution education for coastal stakeholders, may significantly enhance the capacity of coastal stakeholders in ECR. Third, coastal stakeholders in the Great Lakes have an extensive and wide-ranging existing local knowledge, experience and expertise in resolving environmental and resource conflicts. Fourth, a conflict resolution system’s design developed in this study may serve as an integrated framework for the analysis and resolution of environmental and resource conflicts. This ECR system design involves such important components as conducting conflict and stakeholder analysis; identifying the root causes of conflict; bringing conflict participants together to discuss resolution options; and building in continuous evaluation of environmental conflict resolution processes.

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 score0.551
Threshold uncertainty score0.869

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 studies0.0000.001
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 head0.033
GPT teacher head0.241
Teacher spread0.208 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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