Criteria and Indicators for sustainable forest management: lessons learned in the Southern Cone
Bibliographic record
Abstract
The Southern Cone countries of Chile, Argentina and Uruguay have a common background regarding land use and land cover with a total of 46 million ha of forests whose benefits are prospering for the regional framework of the Southern Cone. The three countries do not articulate or interchange on their forest policies beyond circumstantial agreements. In this regard, and as our first research focus, we examined experiences while participating in the international Montréal Process on Criteria and Indicators for the Conservation and Management of Temperate and Boreal Forests. Secondly, we focused on the progress these processes have afforded regarding respective national implementation of criteria and indicators for sustainable forest management (C&I for SFM) and uptake in forest policy. Thirdly, we examined also the obstacles experienced during participation and implementation. We based our findings on content analysis of key documents and author observations. Albeit the institutional and political frameworks between the countries differ, we found common constraints on budgeting, limited human resources and institutional capacity. Communication to society and policy makers' commitment are also important weaknesses. The engagement of the three countries in the Montréal Process and the application of related national sets of C&I for SFM have provided solutions to recent land use conflicts. This also strengthened the quality and effectiveness of recently approved laws and regimes for a sustainable forest management. In conclusion, the forest dialogues of these countries, within and between each other, were reinforced by participation in C&I for SFM processes, helping to bridge the gap between decision-makers, national forest agencies, academia and other forest-related stakeholders. Common indicators and related national reports facilitated the identification of affinities for regional integration on a common basis and helped to raise the level of national forest policies increasing its strength and commitment to global forest challenges. The lessons learned should be considered to reach progress towards sustainability.
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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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| 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".