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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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