Secondary manufacturing in British Columbia: Structure, significance and trends
Bibliographic record
Abstract
Similar to many other jurisdictions, British Columbia (BC) is no longer able to expand forest sector production and employment by drawing upon additional timber reserves, so it is seeking to expand value-added (i.e., secondary) manufacturing in forest products. Given the significance of the forest sector to BC, it is important that decision-makers seeking to promote an expansion in secondary manufacturing have accurate sector information. This paper presents the results of a 1998-99 survey of the BC solid wood secondary manufacturing industry. The project gathered operational, employment, production, marketing and financial information on nine defined product groups of business types (BTs) for 1997. The industry information is analyzed to provide a quantitative and qualitative examination on the current structure and significance of the sector, and a discussion on the major challenges confronting secondary manufacturing. An analysis of sector trends is also provided.Sector employment for nine business types totalled 19 490 person years and total sector sales an estimated $3.87 billion (about 22% of total BC forest product sales). Sales for seven business types (excluding panelboards, shakes and shingles) totalled $2.69 billion, up about 40% from 1994 measured in nominal dollars. Direct employment coefficients for a standard volume of timber equivalent are estimated for each of the business types. Key words: forest industry, value-added, employment, markets, policy
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".