Examining forest governance in the United States through the Montréal Process Criteria and Indicators Framework Ex amination de la gestion forestière aux Etats-Unis en utilisant le cadre des indicateurs et des critères de procédure de Montréal Examinando la gobernanza forestal en los Estados Unidos a través del Marco de Criterios e Indicadores del Proceso de Montreal
Bibliographic record
Abstract
SUMMARY This paper examines laws, policies, organizations and other governance elements and arrangements that influence forest conservation and sustainable resource management in the U.S. through a set of 10 Indicators associated with Criterion Seven of the Montréal Process Criteria and Indicators Framework. The applicability and utility of these indicators as a measure of forest governance at the national level is examined and associated quantitative and qualitative data are presented and discussed. In the U.S., a broad range of laws governs public lands, dictating management processes and practices. Federal and state laws protect wildlife and endangered species on all public and private lands, and foster a range of prescribed and voluntary forest practices to protect water, air, and other public goods and services on private lands. Federal and state laws also provide for technical and financial assistance, research, education, and planning on private forest lands. Market based mechanisms increasingly are used to advance forest sustainability, as are policies, programs, and partnerships that link related policy networks, purposes, and desired outcomes across an expanding range of sectors. Nevertheless, challenges in advancing forest sustainability in the U.S. remain, particularly where incentives for sustainable forest management are low and pressures for development and agriculture are high. Furthermore, while such multilateral agreements help identify common forest goals, develop metrics, and report individual country status, they by no means enforce specific forest practices or ensure good forest governance.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".