Putting community forestry into place: implementation and conflict
Bibliographic record
Abstract
THE IMPLEMENTATION CHALLENGE If a community decides to create a community forest, what comes next? Even arriving at the community forestry decision can be contentious and difficult, but making it a reality poses a range of challenges. At one time there was an assumption that once a policy decision was made, its execution became a simple and mundane affair that did not merit significant attention (Hyder 1984). When it came to program or policy efficacy, it was the quality of the idea, or the correctness of the ideology which gave birth to ideas, that mattered. Policy implementation followed naturally; it was an ordinary process that would have little impact on the success of the policy concept. It is fair to say that some institutions still approach the policy process under this assumption. The understanding of governance and modern government has become more experienced, and a substantial body of research on evaluation has emerged and slowly matured. Audit techniques have also progressed away from an obsession with numbers, and now incorporate qualitative tools that seek to assess efficacy and policy impacts and to understand the social–cultural, contextual and institutional factors that affect policy success. While ideas certainly matter, when it comes to putting them into practice even the best can go awry. In the policy process the implementation stage is without doubt integral to the successful application, and in some respects to the very practicability, of ideas.
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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 | 0.048 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".