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Record W1539151621 · doi:10.22230/jem.2010v10n3a7

Community resiliency: Contribution from the forest resources sector

2010· article· en· W1539151621 on OpenAlexaboutno aff
W. W. Bourgeois

Bibliographic record

VenueJournal of Ecosystems and Management · 2010
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSustainabilityGovernment (linguistics)ProductivitySoftware deploymentForest managementEnvironmental resource managementNatural resource economicsEconomicsEconomic growthEcology

Abstract

fetched live from OpenAlex

Forest-dependent communities in British Columbia are facing a crisis brought about by uncertainty surrounding their future economic viability. For over 45 years, the provincial government has successfully applied the same forest sector model, but it has shown signs of faltering in the last 15 years due to changes involving policies, investors in forest companies, and constraints imposed through the Canada-US Softwood Lumber Agreement. The combination of these influences has resulted in serious deterioration of the connection between the forest companies and forest-dependent communities, thereby affecting the sustainability of these communities. The resiliency of forest-dependent communities (Aboriginal and non-Aboriginal) is critical in this province and depends on a holistic application of environmental, healthand wellness, economic, and human capital within the community to create wealth and productivity. The evolving forest sector must contribute to achieving this goal.As a community struggles with an uncertain future and tough economic times, it requires deployment of its resources in an efficient and effective manner. This is best realized through a strategic approach that begins with identifying a vision and goals for community resiliency. Once this groundwork is established, a strategic plan will help to focus available resources on appropriate actions to achieve resiliency.

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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.189
Teacher spread0.181 · 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 designNot applicable
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
Published2010
Admission routes1
Has abstractyes

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