Scaling-up natural resource management: insights from research in Latin America
Bibliographic record
Abstract
Scaling-up local innovations in natural resource management (NRM) involves learning that is centred around three themes: promoting local-level innovation, understanding why local innovations work in specific contexts, and reflecting on their relevance in other geographical and social contexts. Successful scaling-up depends in part upon the relationships among multiple stakeholders at different levels around this learning. The experiences of researchers supported by the International Development Research Centre (IDRC) provide insights into four questions: What is scaling-up? Why scale-up? What to scale-up? and How to scale-up? The authors propose that scaling-up is a multi-stakeholder process consisting of five components including: framing the context, promoting participation, fostering learning, strengthening institutions, and disseminating successful experiences. Key bottlenecks to scaling-up are the absence of open communication and the mutual recognition among stakeholders of each other's rights, responsibilities, and roles.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| 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".