Examining the social component of sustainable forest management in Prince Albert and Vilhelmina Model Forests
Bibliographic record
Abstract
Due to the forest industry downsizing, many communities in rural forest regions in Canada \nand Sweden are facing problems to survive. In order to create community sustainability, \nresilience and well-being in remote forest regions, the view on the forest resources has \nshifted towards multiple use, through the concept of sustainable forest management (SFM). \nBeside the economic and ecological elements of sustainability, the social forest values are \nneeded, contributing to the human well-being, local participation, stakeholder collaboration, \nhuman rights and cultural connection. \nIn this thesis the embodiment of the social component of SFM within Prince Albert Model \nForest (Canada), and Vilhelmina Model Forest (Sweden) will be examined. Being partners \nand facing similar challenges as rural boreal forest regions, the two model forests are compared \nthrough analysis of projects and activities, conducted interviews and organization \ndocuments. \nLooking at projects mentioned as successful by the interviewees, they all have elements \nfrom the social values of SFM. The direction can be explained by the introduction of the \nForest Communities Program in Canada, demanding the Model Forests to work towards \ncommunity stability and resilience, the Model Forest organization concept itself and the \nway global focus are increasing around social forest values. In the future, it may be important \nthat the role of the MFs enable some kind of political authorization and legitimacy in \norder to improve conflict solving and indigenous rights equality. Funding is crucial to run a \nModel Forest organization, enabling coordination and administration staff, representative \nparticipation and travel possibilities to meetings. \n
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".