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Record W2008722542 · doi:10.1139/x2012-030

Risk, knowledge, and trust in managing forest insect disturbance

2012· article· en· W2008722542 on OpenAlexafffundvenueabout
Bonita L. McFarlane, John R. Parkins, David O. Watson

Bibliographic record

VenueCanadian Journal of Forest Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of AlbertaNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest Service
KeywordsMountain pine beetleDendroctonusRisk perceptionBusinessPerceptionRisk managementEnvironmental resource managementDisturbance (geology)Forest managementPublic trustGovernment (linguistics)Scale (ratio)GeographyPublic relationsForestryPsychologyPolitical science

Abstract

fetched live from OpenAlex

Understanding perceptions of risks, awareness, and trust in management agencies is critical to effective management of large-scale forest insect disturbance. In this study, we examined regional variation in public perceptions of risk, compared public and land managers’ perceptions, and examined knowledge and trust as factors in shaping public perceptions of a mountain pine beetle (MPB) ( Dendroctonus ponderosae Hopkins) infestation. Survey data were collected from residents (n = 1303) in three regions of Alberta and from land managers (n = 43) responsible for MPB management. Results showed that residents had moderate or great concern for MBP risks, they were not well informed about MPB, and they showed slight trust in the provincial government and forest industry to manage the beetle. There was regional variation in perceptions of risks, knowledge, and trust. Land managers were less concerned about nontimber effects and had higher trust than the public. A positive correlation between trust and risk perceptions appears to contradict the risk literature. This relationship may be influenced by an intervening effect of knowledge. These results call for more attention to the content of risk messaging and the effects of trust and knowledge on the general public who take up these messages.

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)0.003
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 score0.725
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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

Citations40
Published2012
Admission routes4
Has abstractyes

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