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Record W2342156439 · doi:10.14288/1.0075515

Are community forests successful in British Columbia? : an evaluation of the socio-economic success of the community forestry in British Columbia using Criterion 6 of the Montréal Process

2016· article· en· W2342156439 on OpenAlexaboutno aff
Chris Boulton

Bibliographic record

VenuecIRcle (University of British Columbia) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsForestryCommunity forestryProcess (computing)GeographyForest managementEnvironmental resource managementEconomicsComputer science

Abstract

fetched live from OpenAlex

Community forestry has recently begun to take hold in British Columbia. Though its origins lay as far back as the 1950’s, official government tenure agreement was only recently introduced in the late 1990s. In the last 5 years the community forestry has grown at an unprecedented rate currently totaling 58 projects either in full operation or at some degree of planning. Some applaud community forests as a way to revive struggling rural communities, while guaranteeing more ecologically friendly land management practices. However, with low lumber prices, concerns about the midterm timber supply, and the rising Canadian dollar, the viability, sustainability and socio-economic success of community forests has come into question. This paper attempts to evaluate the socio-economic success of four community forests of British Columbia using Criteria 6 of the Montréal Process. To be determined successful the community forests had to demonstrate at least 50% fulfillment of indicators within each element of Criterion 6. Furthermore, the community forests were ranked after having been evaluated. Results were compiled into a master table which revealed that according to the paper definition of success; all four of the community forests were determined to be socio-economically successful. However, it was also determined that if these findings were to be extrapolated on to all community forests, additional research and more specific indicators would have to be included.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.028
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.223
Teacher spread0.204 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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