Bibliographic record
Abstract
Forest-dependent communities in British Columbia are facing a crisis brought about by uncertainty surrounding their future economic viability. For over 45 years, the provincial government has successfully applied the same forest sector model, but it has shown signs of faltering in the last 15 years due to changes involving policies, investors in forest companies, and constraints imposed through the Canada-US Softwood Lumber Agreement. The combination of these influences has resulted in serious deterioration of the connection between the forest companies and forest-dependent communities, thereby affecting the sustainability of these communities. The resiliency of forest-dependent communities (Aboriginal and non-Aboriginal) is critical in this province and depends on a holistic application of environmental, healthand wellness, economic, and human capital within the community to create wealth and productivity. The evolving forest sector must contribute to achieving this goal.As a community struggles with an uncertain future and tough economic times, it requires deployment of its resources in an efficient and effective manner. This is best realized through a strategic approach that begins with identifying a vision and goals for community resiliency. Once this groundwork is established, a strategic plan will help to focus available resources on appropriate actions to achieve resiliency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".