MétaCan
Menu
Back to cohort
Record W2970136003 · doi:10.1139/cjfr-2019-0026

Quantifying and easing conflicting goals between interest groups in natural resource planning

2019· article· en· W2970136003 on OpenAlexvenueno aff
Kyle Eyvindson, Anna Repo, María Triviño, Sari Pynnönen, Mikko Mönkkönen

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsForest managementCompromiseBusinessEnvironmental resource managementVariety (cybernetics)Public interestNatural resourceWood productionEcoforestryBiodiversityLoggingForest ecologyNatural resource economicsEcosystem servicesInterest groupResource (disambiguation)ForesterSustainable forest managementConflict of interestProduction (economics)Intact forest landscapeEcosystemEconomicsGeographyEcologyForestryPolitical science

Abstract

fetched live from OpenAlex

Management of natural resources at the regional level is a compromise between a variety of objectives and interests. At the local level, management of the forests depends upon the ownership structure, with forest owners using their forests as they see fit. A potential conflict occurs if the forest owners’ management decisions are counter to the interests of society in general or the industry that relies on the forest resource as their raw material. We explore the intensity of this conflict at the regional level in several large boreal forest production landscapes. To explore the conflict, we investigate three main interest groups: (i) economically oriented forest owners; (ii) industry groups (focusing on maintaining an even timber supply); and (iii) a group representing general public interests (focusing on enhancing ecosystem services and biodiversity protection). The severity of conflicts differs between interest groups; we found a minor conflict between the economically oriented forest owners and industry and a severe conflict among general public interests and the other groups. By quantifying the conflicts, visualizing the impacts shared among interest groups, we anticipate that through shared discovery and understanding, forests can be managed to lessen the conflicts between interest groups.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.987

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.369
Teacher spread0.255 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Management and PolicyFrench-language works237,207