The International Model Forest Network (IMFN): Elements of Success
Bibliographic record
Abstract
The International Model Forest Network (IMFN) was announced by Canada at the Rio (UNCED) Summit ten years ago to pilot outside of Canada a promising participatory field-level approach to sustainable forest management then being developed in Canada through its national model forest network. The IMFN has since grown from three sites in two countries (outside of Canada) in 1994 to 19 sites in 11 countries, in addition to numerous additional sites proposed and at early stages of development. Now with the successor event to Rio, the World Summit on Sustainable Development, about to take place in Johannesburg, South Africa, there is an appropriate milestone at which to pause and consider its evolution and growth. Among the elements of success attributed to the growth of the network the authors consider the nature of the approach itself as being an innovative re-formulation of widely shared management values, its flexibility across borders and ecosystems, and the support provided in its development by the Canadian Model Forest Network and other domestic and international partners. The article looks at similarities and differences between the international and Canadian applications of this approach and describes some of the lessons learned and difficulties met in applying the approach internationally. Key words: IMFNS, CMFN, International, Networking, Asia, Latin America
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".