Are forest sector firms maximizing the economic returns from their timber? Evidence from British Columbia
Bibliographic record
Abstract
Understanding the components of the forest value chain and linkages is essential in designing a system that will maximize the economic value of Canadian fibre. A key part of the system is how firms incorporate the fibre quality and attributes of their timber supply into the decision over what kinds of products to manufacture. The linkage between timber supply and how firms decide to utilize fibre is critically important, especially in Canada, where government policy plays a key role in governing access to fibre. We explore this question by looking at whether firms try to maximize the economic return from their fibre, or instead focus on other objectives such as maximizing the production volume they can generate from their timber supply. We surveyed sawmills and woodland managers in British Columbia in the Fall of 2006 and focused on a particular characteristic—the extent to which sawmills and operations are responding to value-based signals rather than to other kinds of signals. We found that the majority of BC forest sector firms we interviewed are emphasizing volume-based measures on a daily basis, whether they are in sawmill or woodlands operations, and while economic measures become more important as the period lengthens, it is unclear as to how firms reconcile these 2 different types of measures. Key words: organizational behaviour, firm operations, Canadian forest industry, value chain optimization
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".