Does co-operation among small forest operators lead to economic benefits? A Saskatchewan case study
Bibliographic record
Abstract
Independent operators (IOs) in Saskatchewan are small forest business owners with timber allocation volumes under 20 000 m3. The group is characterized by above-average industry wood procurement and transaction costs that in the past were compensated by above-average market prices in conjunction with limited competition. Recently, increased competition confounded by low demand, low prices, and increasing operating costs have made it necessary for IOs to restructure to remain competitive. This research investigates the effects of restructuring IO business using a new generation cooperative model (NGC). We use a comparative analysis of a business-as-usual fibre procurement cost model and an NGC fibre procurement cost model to determine the economic effects of co-operating. Data were obtained from the IOs to generate fibre procurement cost models. The results of this analysis reveal that the co-operative model has the potential to provide significant economic benefits to IOs through the creation of economies of scope in harvesting costs, but has little effect on other costs. The analysis also reveals that so long as the NGC consists of IOs that require both large and small diameter fibre, the IO NGC has the potential to provide significant economies of scope in fibre utilization.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".