The Problematique of Community-Based Conservation in a Multi-Level World
Bibliographic record
Abstract
"Community-based resource management or community-based conservation is not just about communities. It is about governance that starts from the ground up and involves multi-level interactions. Complexities of this multi-level world create problems but also provide opportunities to combine conservation with development. I unpack the problematique of community-based conservation and deal with four aspects of it. The first is the inability and discomfort of our conventional science and resource management to deal with multiple objectives. Many projects are either primarily about conservation or primarily about development, but rarely both. Second, community-based approaches to conservation have rarely employed strong deliberative processes. 'Conservation', as conceived at the local level, tends to be different from 'conservation' as conceived by international conservation organizations. A multi-lens approach is needed whereby communities become partners (and not the objects) of conservation projects. Third, the field of conservation has not made good use of the lessons from commons theory. Much of so-called community-based conservation of the last 10-15 years has been half-hearted, misdirected, and theory- ignorant. Finally, we can do a better job conceiving, researching and analyzing community-based conservation in terms of scale, organization, uncertainties and dynamics. Community-based conservation in a multi-level world is a complex systems problem and should use the tools and approaches appropriate for dealing with complexity."
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.071 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".