Feasibility of community forestry in northern Ontario : a socio-economic and biophysical evaluation framework / by Patrick W. Matakala
Bibliographic record
Abstract
Community forestry has become a much-discussed form of \nforest land tenure and management in Northern Ontario. It \nis a viable approach to community economic development \nespecially among communities that are dependent on the \nforest sector. This study is a broadly-based investigation \nof the socio-economic and biophysical factors that give \ncommunities an inherently high potential for success in new \ncommunity forestry ventures. The factors identified herein \nhave been arranged into a framework which I propose \ngovernment can identify those communities where community \nforestry may have a high chance of succeeding. The factors \nattributing to the success of the North Cowichan community \nforest in British Columbia have been presented for \ncomparative purposes. A total of 15 variables have been \nexamined in this study. This study area covers sections of \nOntario Ministry of Natural Resources? (OMNR) former \nNorthern, North Central, and Northeastern Regions of \nOntario, altogether encompassing 22 communities. Based on \nthe results of the study, the communities of Nipigon, \nGeraldton, Hearst, Wawa, and Marathon would be excellent \ncandidates for pilot projects or in-depth feasibility \nstudies on community forestry. The second group of \ncommunities that may be considered are Terrace Bay, White \nRiver, and Red Rock. I conclude that community forestry is \na viable option for forest land tenure and management in \nsome communities (with high inherent success potential) in \nNorthern Ontario.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".