Bibliographic record
Abstract
Log exports have been an important issue for the forest industry of coastal British Columbia, which is the source of a majority of the province’s log exports. They account for a relatively small proportion of the province’s annual harvest, normally less than 5%, yet garner a lot of attention from the government, industry and the public. British Columbia log export policies were first enacted in 1891. Policies were introduced to restrict log exports in order to promote the development of the domestic manufacturing industry. Surplus to domestic manufacturing has been the main criterion for receiving an export permit since the government introduced exemptions to the restrictions in 1901. Log export volumes tend to increase during recessionary times to stimulate economic activity in the forest industry and normally rise and fall inversely with the demand for manufactured wood products. In 1995 the largest importer of forest products from coastal British Columbia, Japan, went into recession. Following that, Japanese builders moved away from the use of hemlock lumber when new building codes were introduced there. This caused lumber exports to Japan to collapse and the price of hemlock, which makes up 60% of the inventory on the coast, to go down by almost half in three years. The coastal forest industry has been struggling since then. The volume of logs exported from the coast has increased dramatically from the lows of 1997 and many coastal mills have shutdown. This has brought a lot of attention to log export policy. A common perception is that log exports are akin to exporting manufacturing jobs. Log exports increase harvesting activity by giving companies access to markets that are willing to pay more for logs than domestic manufacturers can afford. Without access to export markets many stands would not be harvested because the domestic log price is below the cost of harvesting them. The logs that are not exported, which was 76% in 2010, are sold domestically. Exports essentially subsidize domestic log prices and generate harvesting activity that would not have occurred otherwise. All sectors of the forest industry benefit from fewer log export restrictions.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.049 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".