Traditional agroecological knowledge, adaptive management and the socio-politics of conservation in Central Sulawesi, Indonesia
Bibliographic record
Abstract
This paper illustrates the opportunity for conservation offered by linking traditional agroecological knowledge and advances in adaptive management theory and practice. Drawing on examples from the Banawa-Marawola region of Central Sulawesi, Indonesia, a suite of traditional resource management practices premised on principles of adaptive management are identified and assessed, including: (1) resource management practices and regulations that are associated with the dynamics of complex systems; (2) procedural, planning and decision-making processes that foster learning; (3) sanctions and taboos that act as social mechanisms for the management and conservation of natural resources; and (4) ceremonies and social interactions that promote cultural internalization of the various practices, procedures and mechanisms. In addition, an emerging socio-political movement in the Banawa-Marawola region is explored. Premised on the strengthening of traditional rights and practices, the nascent Kamalise movement potentially provides the socio-political, institutional and organizational context needed to link traditional agroecological knowledge and adaptive management with broader conservation goals. Based on this analysis, two opportunities to enhance conservation in the region are identified: first, maintaining traditional agroecological systems and the associated adaptive resource management strategies used by local groups, and second, building upon the Kamalise movement to forge conservation alliances among communities, non-government and government organizations in which locally-evolved adaptive resource management strategies can be effectively applied. Both opportunities to combine traditional knowledge, adaptive management and conservation, however, are linked to the development aspirations of traditional groups: self-determination, acquisition of land rights and controlling the impacts of changes in livelihood.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".