Addressing the Conceptual Controversy of Sustainable Intensification of Agriculture: A Combined Perspective from Environmental Philosophy and Agri-Environmental Sciences
Bibliographic record
Abstract
During the last 20 years, agronomists, environmentalists and related researchers have conveyed the need of producing enough food to satisfy the growing population demand, with minimum environmental footprint. Under this framework, the need for a “sustainable intensification” (SI) of agriculture has arisen, being a concept deeply contested the last several years. We aim to shed some light on the matter from the point of view of both environmental philosophy and agri-environmental sciences. We found that the lack of clarity exposes the conceptual limits of SI, since its attributions are far from being extrapolated, for example, to animal production. Agricultural science should ensure that stakeholders understand the facts and implications of SI before implementing them. In addition, if understood only as either a set of practices or a sort of panacea, SI will be closer to fail for stakeholders’ expectations. Then, a key concern we have highlighted is one which should compel agri-environmental scientists and environmental philosophers alike to hold such conceptual frameworks accountable. Ensuring communities and public actors make informed choices about food security requires that shared goals between our disciplines are enacted in research, with community well-being as a core consideration of any debate regarding sustainability.
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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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.087 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| 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".