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Record W6991285232

Flexible Specialization and Green Entrepreneurship: Secondary Wood Processing in the Vancouver Metropolitan region

2012· dissertation· en· W6991285232 on OpenAlexfundaboutno aff

Bibliographic record

VenueSummit (Simon Fraser University) · 2012
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsMetropolitan areaCertificationWood processingWood industryLicenseCertified wood
DOInot available

Abstract

fetched live from OpenAlex

This research assesses the location behaviour and environmental performance of 'value-added' wood processing activities in Vancouver Metropolitan region.Conceptually the study is informed by an integration of the flexible specialization model with green entrepreneurship.Empirically, the study adopts an extended case study approach and is based on in-depth semi-structured interviews with respondents of 41 small firms representing the major sub-segments of the value-added wood products industry in the Vancouver Metro region as well as research and industry associations.These interviews were stratified among three zones: the inner-city and suburbs, outer suburbs, and the Fraser Valley.The study found that these firms perceive diverse location advantages and disadvantages and are flexibly specialized to some degree with respect to local entrepreneurship, access to local labour pools, diverse markets, material supplies and external economies.As green entrepreneurs, they are adopters rather than leaders, and use and awareness about certification schemes is varied.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.469
Threshold uncertainty score0.944

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.217
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 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

Citations0
Published2012
Admission routes2
Has abstractyes

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