Sustainable Agri-Food Systems: Environment, Economy, Society, and Policy
Bibliographic record
Abstract
Agri-food systems (AFS) have been central in the debate on sustainable development. Despite this growing interest in AFS, comprehensive analyses of the scholarly literature are hard to find. Therefore, the present systematic review delineated the contours of this growing research strand and analyzed how it relates to sustainability. A search performed on the Web of Science in January 2020 yielded 1389 documents, and 1289 were selected and underwent bibliometric and topical analyses. The topical analysis was informed by the SAFA (Sustainability Assessment of Food and Agriculture systems) approach of FAO and structured along four dimensions viz. environment, economy, society and culture, and policy and governance. The review shows an increasing interest in AFS with an exponential increase in publications number. However, the study field is north-biased and dominated by researchers and organizations from developed countries. Moreover, the analysis suggests that while environmental aspects are sufficiently addressed, social, economic, and political ones are generally overlooked. The paper ends by providing directions for future research and listing some topics to be integrated into a comprehensive, multidisciplinary agenda addressing the multifaceted (un)sustainability of AFS. It makes the case for adopting a holistic, 4-P (planet, people, profit, policy) approach in agri-food system studies.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".