Non-timber forest products in community development: Lessons from the Russian Far East
Bibliographic record
Abstract
One of the components of the IUCN – The World Conservation Union project, "Building Partnerships for Forest Conservation and Management in Russia" funded by the Canadian International Development Agency (CIDA), is designed to assist remote communities of the Russian Far East to sustainably develop their NTFP resources. In our project, NTFPs are viewed as one part of a local sustainable livelihood strategy (including tourism, cultural activities, hunting, herding). We provide business and legal issues training, consultation on small business and community-based enterprise development, and support for sustainability and monitoring programs. One of the basic principles of the project has been a participatory approach to project development and implementation. It is the hope of project participants that the successful development of NTFP and other opportunities will decrease the pressure to move forward with potentially damaging resource exploitation activities. Although community economic development is the primary goal, the revival and sharing of indigenous knowledge about NTFPs has been identified by participants as a key issue, and is a focus of educational materials being developed through the project. Key words: non-timber forest products (NTFPs), community economic development, sustainable use of natural resources, Native communities, traditional knowledge, the Russian Far East, Kamchatka, Sakhalin, boreal forests, IUCN – The World Conservation Union, Canadian International Development Agency (CIDA)
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".