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Record W1967014610 · doi:10.1080/13549830802522087

Community forestry in British Columbia, Canada: the role of local community support and participation

2009· article· en· W1967014610 on OpenAlexafffundabout
Kirsten McIlveen, Ben Bradshaw

Bibliographic record

VenueLocal Environment · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of GuelphSimon Fraser University
FundersSimon Fraser University
KeywordsCommunity forestryCommunity developmentCommunity economic developmentScholarshipVariety (cybernetics)RevenueCommunity organizationPolitical scienceCommunity participationCommunity buildingCommunity engagementPublic relationsEnvironmental resource managementBusinessPublic administrationEnvironmental planningForest managementSociologyForestryGeographyFinanceEconomicsSocioeconomics

Abstract

fetched live from OpenAlex

Drawing on the example of community forestry in British Columbia, Canada, this paper conceptualises and empirically assesses key contributors to successful community economic development, with a special emphasis on the role of local community support and inclusive participation. Relevant scholarship highlights the importance of these factors for achieving success with any community economic development (CED) initiative. While the initial experiences of 10 community forest initiatives under British Columbia's Community Forest Pilot Project offers evidence to corroborate this view, it is also evident that expertise and leadership, even of the exclusive variety, can substitute for community support and participation, if the goal of CED is to create a profitable community enterprise capable of delivering jobs and revenues to community members.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0170.006
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.197
Teacher spread0.189 · 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 designQualitative
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

Citations22
Published2009
Admission routes3
Has abstractyes

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