Institutional Perspectives of Climate-Smart Agriculture: A Systematic Literature Review
Bibliographic record
Abstract
Climate-smart agriculture (CSA) is increasingly seen as a promising approach to feed the growing world population under climate change. The review explored how institutional perspectives are reflected in the CSA literature. In total, 137 publications were analyzed using institutional analysis framework, of which 55.5% make specific reference to institutional dimensions. While the CSA concept encompasses three pillars (productivity, adaptation, and mitigation), the literature has hardly addressed them in an integrated way. The development status of study sites also seems to influence which pillars are promoted. Mitigation was predominantly addressed in high-income countries, while productivity and adaptation were priorities for middle and low-income countries. Interest in institutional aspects has been gradual in the CSA literature. It has largely focused on knowledge infrastructure, market structure, and hard institutional aspects. There has been less attention to understand whether investments in physical infrastructure and actors’ interaction, or how historical, political, and social context may influence the uptake of CSA options. Rethinking the approach to promoting CSA technologies by integrating technology packages and institutional enabling factors can provide potential opportunities for effective scaling of CSA options.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".