The southwestern United States: community forestry as governance
Bibliographic record
Abstract
This chapter was first subtitled Community forestry as environmental governance . As we worked though the history of community forestry in the American west, and looked into the case study literature, it became apparent that the word environmental might be limiting. In part this is because the term often carries the suggestion of preservation; the word can be more divisive than inclusive, which is unfortunate but nevertheless a reality. The definition of environmental governance, at least the one we hold out here, is based on the notion of inclusiveness manifest in processes that integrate diverse community values into decision-making about environmental resources – all with the intent of maintaining the integrity and resilience of natural systems that provide the basis for human wellbeing. A tall order to be sure. Such ideas are quite at home with fundamental notions of community forestry as a framework that respects the interdependence of forest landscapes and forest communities, and contains integrated objectives and strategies based on the equally important social, economic and ecological qualities of places (Baker and Kusel 2003; Wycoff-Baird 2005; Padgee et al . 2006). This “three-legged stool,” as Baker and Kusel (2003) label it, is certainly akin to the three pillars of sustainability. Identifying the governance model that would actually implement the three-legged model is a trickier undertaking, but one many have attempted. It is often in these efforts that we see the fundamental differences in approaches to community forestry that emerge in different places. The institutional and administrative contexts within which community forestry is implemented are highly variable, even within one nation such as the United States, and the ideal of community control, a common theme in community forestry discourses, may not be possible in many places unless there are significant, even radical changes to institutional structures. Such changes would also have to reflect the regional qualities that exist with the United States, despite the reality that the macro-institutions that manage public forest lands are in many respects homogenous and often distant from the locales and resources they govern. Non-homogenous regions governed by distant homogenous institutions: this is not exactly a good formula for responsive community resource management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".