Stakeholder perceptions of scientific knowledge in policy processes: A Peruvian case-study of forestry policy development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract There is a need to better understand how scientific knowledge is used in decision-making. This is especially true in the Global South where policy processes often occur under high political uncertainty and where a shift toward multilevel governance and decision-making brings new opportunities and challenges. This study applies knowledge-policy models to analyse a forestry research project that succeeded in influencing national policy-making. We investigate how decisions were made, what factors affected and shaped the policy process, and how scientific knowledge was used. The results highlight the complexity of policy processes and the related challenges in crossing the science-policy interface. Perceptions of scientific knowledge differed greatly among stakeholders, and those perceptions strongly influenced how scientific knowledge was valued and used. The findings suggest a need for researchers to better understand the problem context to help design and implement research that will more effectively inform decision-making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it