The implications of agroecology for meeting the sustainable development goals (SDGs): a scoping review
Bibliographic record
Abstract
Agroecology’s multidimensional theorization and transdisciplinary practices position it as a promising paradigm for addressing challenges within the industrial food system and therefore contributing to the achievement of the SDGs. Despite the growing research of agroecology across geographical contexts, there is limited study about the ways in which agroecology is directly and indirectly linked to the SDGs. This scoping review categorizes carefully screened agroecology studies in light of agroecology’s three dimensions and critically examines their geographical and thematic foci when illustrating the connections with each of the SDGs. Our research finds that most studies examine agroecology in the context of the Global South and take a hybrid perspective that describes agroecology as science, practice and/or movement, highlighting its multidiensional nature. Yet, there are limited studies that examine the connection with certain SDGs (i.e. SDG 9, 10, 14 and 16). Moreover, instead of stating explicitly how agroecology contributes to SDGs, existing studies often provide passing reference to the SDGs and do not use strong empirical evidence to illustrate the contribution. The research points out new spaces for agroecology studies by highlighting the need for research that directly connects agroecology to the SDGs with strong empirical evidence. We argue that policymakers and practitioners need to integrate agroecology into development strategies to address systemic inequalities within food systems and advance sustainability goals.
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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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".