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Record W2113740671 · doi:10.1139/x07-140

Assessing innovativeness in the North American softwood sawmilling industry using three methods

2008· article· en· W2113740671 on OpenAlexvenueaboutno aff
Chris Knowles, Eric Hansen, Steven R. Shook

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwoodScale (ratio)MarketingProfit (economics)MathematicsAgricultural scienceStatisticsBusinessEngineeringPulp and paper industryEconomicsEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

Using a mail questionnaire targeted at 500 softwood sawmills in the United States and Canada, firm innovativeness was assessed using three methods: (1) current technology, (2) self-evaluation, and (3) a new scale — the propensity to create and adopt scale. The results of these three methods were then compared to assess the performance of each method. Additionally, the relationship between firm innovativeness and financial performance was examined. Based on responses from 85 sawmills (19% adjusted response rate), the results show that both the self-evaluated and the propensity to create and adopt measures differentiate between mills with high and low levels of innovativeness. The composite of the propensity to create and adopt scale shows higher reliability (Chronbach’s α = 0.97) than the self-evaluated scale (Chronbach’s α = 0.68). Significant relationships between sawmill performance and each of the three measures of innovativeness were seen, with the propensity to create and adopt scale generally having the strongest positive relationships. Current technology was significantly related to sales growth, but not gross profit.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.223
GPT teacher head0.440
Teacher spread0.217 · 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 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

Citations58
Published2008
Admission routes2
Has abstractyes

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