Changing resource management paradigms, traditional ecological knowledge, and non-timber forest products.
Bibliographic record
Abstract
—We begin this paper by exploring the shift now occurring in the science that provides the theoretical basis for resource management practice. The concepts of traditional ecological knowledge and traditional management systems are presented next to provide the background for an examination of resilient landscapes that emerge through the work and play of humans. These examples of traditional ecological knowledge and traditional management systems suggest that it is important to focus on managing ecological processes, instead of products, and to use integrated ecosystem management. Traditional knowledge is often discussed by resource management agencies as a source of information to be incorporated into management practice; in this paper we go further and explore traditional knowledge as an arena of dialogue between resource managers and harvesters. To enter into this dialogue will require mutual respect among managers and users for each others’ knowledge and practice. Such a dialogue could move forest management paradigms beyond our current view of “timber or parks” and toward one of truly
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".