Crossing the science-policy interface: Lessons from a research project on Brazil nut management in Peru
Bibliographic record
Abstract
There are high expectations for contemporary forestry research, and sustainability research more broadly, to have impact in the form of improved institutions, policy and practice and improved social and environmental conditions. As part of this trend, there has been an evolution of research approaches that move beyond isolated, reductionist, disciplinary science toward approaches that integrate disciplines (interdisciplinary) and that engage a wider range of research stakeholders (transdisciplinary) as a way to be more effective. While these approaches evolve, there are good opportunities to learn from the experience of projects that have had impact at some level. This paper presents lessons from a case-study of a research project that succeeded in crossing the science-policy interface. Our study characterizes the design and implementation of a research project on the influence of timber harvesting on Brazil nut production using transdisciplinary research (TDR) design principles, and empirically assesses project outputs and outcomes in relation to a project theory of change (ToC) based on document review and key informant interviews. The Brazil Nut Project included some TDR elements and realized a substantial part of its ToC. The interviews identified mixed perceptions of the research design, implementation and the extent of outcomes achievement from different stakeholder perspectives. Our analysis suggests that limited stakeholder engagement was a crucial factor affecting perceptions of legitimacy and relevance, the two main TDR principles underpinning the overall research effectiveness in our study. The application of the TDR analytical framework indicates substantial scope to improve research effectiveness, even without striving for a TDR theoretical ideal.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 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".