Science for Place-based Socioecological Management: Lessons from the Maya Forest (Chiapas and Peten)
Bibliographic record
Abstract
The role humans should play in conservation is a pervasive issue of debate in environmental thinking. Two long-established poles of this debate can be identified on a preservation-sustainable use continuum. At one extreme are use bans and natural science-based, top-down management for preservation. At the other extreme is community-based, multidisciplinary management for sustainable resource use and livelihoods. In this paper, we discuss and illustrate how these two strategies have competed and conflicted in conservation initiatives in the Maya forest (MF) of the Middle Usumacinta River watershed (Guatemala and Mexico). We further argue that both extremes have produced unconvincing results in terms of the region's ustainability. An alternative consists of sustainability initiatives based on place-based and integrated-knowledge approaches. These approaches imply a flexible combination of disciplines and types of knowledge in the context of natural human interactions occurring in a place. They can be operationalized within the framework of sustainability science in three steps: 1) characterize the contextual circumstances that are most relevant for sustainability in a place; 2) identify the disciplines and knowledge(s) that need to be combined to appropriately address these contextual circumstances; and 3) decide how these disciplines and knowledge can be effectively combined and integrated. Epistemological flexibility in the design of analytic and implementation frameworks is key. Place-based and integrative-knowledge approaches strive to deal with local context and complexity, including that of human individuals and cultures. The success of any sustainability initiative will ultimately depend on its structural coupling with the context in which it is applied."
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".