Experiments in Rural Development and Ecosystem-based Management: the \nPossibilities of Community Forestry in Newfoundland
Bibliographic record
Abstract
The pulp and paper industry and the forestry sector in general, have declined in Newfoundland. In 2005, the Stephenville mill closed; in 2009, the Grand Falls mill closed; in recent years, the remaining mill in Corner Brook has shut down two of its four paper machines and gone through labour disputes and land relinquishments. The Department of Natural Resources, Forestry Branch (DNR Forestry) has based forest management and planning on the pulp and paper industry – for example, only pulp and paper companies can establish long-term leases on Crown lands. Since the once-dominant pulp and paper industry has left entire regions of the province, there are opportunities for new tenure types and new forest management. Community forestry is one such possibility that holds great promise for rural development and sustainable forestry. \n \nCommunity forestry consists of giving a community-based group the right to manage the forest, and the right to determine who benefits from forest products. Details of ownership and lease arrangements would be up to the province and to the communities involved, but could be based on pulp and paper leases, where a Community Forest Authority would assume control of the land and create forest management plans, working alongside agencies such as DNR Forestry, Wildlife, Tourism, and others. \n \nCommunity forestry has demonstrated successes in other provinces, though the structure of a community forest would need to be adapted for Newfoundland, where residents have strong economic and social links to the forest through domestic fuel wood and saw log harvests, moose hunting, fishing, cabin building, and recreational activities. Possible benefits of a community forest include expanding the decision-making power of local residents and communities over nearby forests; linking rural economic development with natural resource management; and providing a platform for discussing and resolving land use conflicts. \n \nA community forest was recently proposed in the Great Northern Peninsula, but it has a long way to go before it is actually created. Its proponents, including the St. Barbe Development Association, recognize many of the benefits of community forestry, but cannot move forward without the support of the provincial government. Though significant obstacles remain, the Great Northern Peninsula Community Forest could provide a model for communities across the province, integrating local community voices with economically, socially, and environmentally sustainable forestry. \n \n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".