Bibliographic record
Abstract
While conservation authorities are well-established in Ontario and have received international recognition (e.g. Krause et al. 2000), British Columbia community forests are less developed and have a shorter history. These two approaches have, however, shared similarities in policy and practice. They are similar in their orientation to forest, water, and soil resources; both tend to inherit degraded land bases; as local resource agencies, each holds an intermediate role between residents and senior governments; each has provincially assigned management rights over lands that represent significant, often contentious, community values. A main difference is that conservation authorities in Ontario represent a provincial-municipal partnership, in principle, based on provincial funding and technical support, while community forests are to be self-sufficient and pay Crown timber harvesting fees (or “stumpage”) to the province. The British Columbia government, with its community forest model, is pursing community forestry to provide economic opportunities for communities, not to create more parks. However, an expanded role for community forests in conservation, hazard management, and recreation is conceivable, given shifting public forest values and growing interest in local control (Robinson et al., 2001). Community forests Ongoing discussion of community forestry remains focused on substantive and …
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".