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Record W2892150459

Understanding SME Success in the Value-added Forest Products Sector: Insights from British Columbia

2018· article· en· W2892150459 on OpenAlexaffabout
Philip Grace, Robert Kozak, Harry W. Nelson

Bibliographic record

VenueBioProducts Business · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBusinessProduct (mathematics)Forest productValue (mathematics)Added valuePoint (geometry)MarketingForest managementForestryGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

Small and medium-sized enterprises (SMEs) in the value-added forest products sector play an important social and economic role in Canadian forest-dependent communities. In British Columbia (BC), the sector is not reaching its full potential. Many factors limit or enable growth of the value-added forest products sector in BC. This study seeks to assess what factors are most integral to success through an in-depth examination of four value-added forest product sector SMEs in rural BC representing four different types of firms with varying levels of performance. The results of the study indicate that, though factors typically considered vital such as access to skilled labour, fibre supply, location, and financial capital are integral to business success, business management skill and firm size are integral and often-overlooked factors. The results of this study point to a need for a better province-wide understanding of the barriers to success commonly faced by forest products SMEs, particularly barriers to management skill development.

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 score0.035
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0080.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.221
Teacher spread0.179 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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