Equity, well-being and ecosystem health: participatory research for natural resource management
Bibliographic record
Abstract
A new vision of humanity’s relationship to nature is gathering strength. We are beginning to see our world as a living system, in which we ourselves are embedded. And we are gaining a greater awareness of our dependence on nature’s ecological services and on one other. Our emerging mental map of the world shows it as an integrated whole rather than a collection of parts. Of course, much human behavior suggests otherwise. As a result of our activities, the earth’s forests are receding, while its deserts are expanding. Topsoil is diminishing, and the ozone layer, which protects us from ultraviolet radiation, is being depleted. Concentrations of heat-trapping gases in the atmosphere are rising, while the numbers of plant and animal species are shrinking. The human population continues to expand, as the gap between rich and poor widens. Nonetheless, people in all walks of life are realizing that the major problems of our time are interconnected and that the only viable solutions are those that satisfy today’s needs without diminishing future prospects. We have made a start toward building sustainable communities, in which we can fulfil our own aspirations while leaving a healthy world for tomorrow’s children. To make this vision a reality means creating equitable social and economic arrangements that protect and maintain the ecological services that earth provides--and learning to live within our ecological means. This publication shows how scientists from centers supported by the Consultative Group on International Agricultural Research (CGIAR) are working with farmers, communities, and organizations to improve the health and well-being of people and our environment. Toward this end the various projects described here are developing and practicing innovative participatory approaches for research on natural resource management (NRM). This research deals with such issues as resource monitoring, policy and legal frameworks, participatory learning, collective resource management, and learning communities. The case studies presented in this publication demonstrate the critical role of participatory approaches in NRM research, highlighting the roles of different stakeholders, the significance of scales and time dimensions, the inevitability of tradeoffs, and the challenges of 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 | 0.189 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".