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Record W2062654397 · doi:10.1139/x08-146

Public priorities for sustainable forest management in six forest dependent communities of British Columbia

2008· article· en· W2062654397 on OpenAlexaffvenueabout
Robert Kozak, Wellington Spetic, Howard W. Harshaw, Thomas C. Maness, Stephen R.J. Sheppard

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSustainable forest managementForest managementThurstone scaleEnvironmental resource managementScale (ratio)Certified woodSustainable managementGeographyPublic participationBusinessEnvironmental planningRecreationEcoforestryForest ecologyIntact forest landscapeSustainabilityEcologyForestryPolitical scienceEnvironmental sciencePublic relationsPsychology

Abstract

fetched live from OpenAlex

It is critical to understand how the public prioritizes multiple forestry values when establishing objectives for sustainable forest management. While this is a complex and difficult task, a necessary step is to elicit a broad range of public opinions in forest planning to ensure that decisions serve the needs of various forest stakeholders and society at large. This study seeks to understand how six forest dependent communities in British Columbia prioritize a number of attributes associated with sustainable forest management by using a simple survey-based measurement tool, the Thurstone scale. The results suggest that ecological attributes are a higher priority for survey respondents followed by quality of life, global warming, and economic considerations. This paper explores some of the ramifications of the priorities for sustainable forest management measured in these six communities as well as implications for using the Thurstone scale in processes like Public Advisory 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.047
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.281
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2008
Admission routes3
Has abstractyes

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