Assessing innovativeness in the North American softwood sawmilling industry using three methods
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".