Growth of secondary wood manufacturing in British Columbia, Canada
Bibliographic record
Abstract
Comprehensive survey data collected on British Columbia’s secondary manufacturing sector at the start and end of the 1990s was examined to develop estimates of sector growth. The measures of growth estimated include changes in the volume of wood processed and sector sales. Underlying explanatory data were examined to find strengths and constraints to future growth under the backdrop of international trade restrictions on lumber for two of British Columbia’s three historically important export markets. In general, the public policy goal of increased secondary manufacturing activity was achieved through the 1990s, with strong growth in both average firm size and in the number of firms participating. Most of the sector growth was for export to the U.S. market, although both domestic and Asian market sales increased as well. The forest sector in the Canadian province of British Columbia (BC) is a key economic driver, generating total sales of nearly $16 billion (all dollar amounts in this article are Canadian dollars) in 2001, providing for 79 percent of provincial manufacturing shipments and 90,000 direct jobs. Maintaining this level of activity in “primary” forest products in the future is unlikely for a variety of reasons, including pressures to preserve native forests, the international emergence of low-cost plantation products and cumbersome trade restrictions with our largest export market, the United States. Maintaining economic and employment benefits from the forest sector requires a maximization of output value per unit of fiber cut. One strategy put forward to accomplish this goal is the expansion of secondary manufacturing in forest products. Given the significance and potential of the BC forest sector, it is important that decision makers examining policy alternatives for secondary manufacturing expansion have accurate and timely information. Information is scarce for this sector in terms of either baseline data or sector growth for any region in Canada, including BC. The main objective of this study was to help fill this knowledge gap through an examination of sector change using data collected in two comprehensive surveys of the secondary manufacturing sector in BC. These surveys were conducted at the start and the finish of the 1990s, a turbulent decade for forestry in Canada, characterized by two different trade actions placed on lumber exports by Canada’s largest trading partner, the United States. The study provides time series results including measures of growth, details on where growth has occurred, and changes in characteristics of the sector through the 1990s. One important result from this analysis is that sector growth occurred primarily through increased sales into the U.S. market.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".