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Public versus expert opinions regarding public involvement processes used in resource and wildlife management

2010· article· en· W1502961536 on OpenAlexaff
Stephen E. Decker, Alistair J. Bath

Bibliographic record

VenueConservation Letters · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsMemorial University of Newfoundland
FundersBundesamt für Naturschutz
KeywordsRepresentativeness heuristicPublic involvementWildlifeResource (disambiguation)Public opinionPublic relationsBusinessPublic landWildlife managementTask (project management)Environmental resource managementPsychologyPolitical scienceEcologySocial psychologyEconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract Successful public involvement efforts can reduce conflict and build trust between resource managers and the public. Differences of opinion between the public and wildlife managers regarding the importance of various characteristics and methods of public involvement have implications for managers wishing to design effective public involvement processes. We compared and contrasted the preferences of German public and European experts in large mammal management regarding characteristics and methods of public involvement. Expert participants attributed high levels of importance to including scientific information in decision making while general public respondents attributed high levels of importance to the cost effectiveness and representativeness of the public involvement effort. Differences were also observed regarding public involvement methods. Experts preferred task forces and advisory groups while the general public preferred information materials and public meetings. We discuss the likely causes of similarities and differences between these two groups and examine the consequent implications for managers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.998

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.262
Teacher spread0.174 · 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 designNot applicable
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

Citations19
Published2010
Admission routes1
Has abstractyes

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