Bibliographic record
Abstract
Agroecology – as a scientific discipline and as an approach to sustainable farming practice – has objectives similar to those of organic agriculture. The paper sharpens the profile of both concepts and identifies strengths and weaknesses. The overarching challenge of both is to minimize trade-offs between food and fiber production on the one hand and non-commodity ecosystem services on the other hand. A comparison of the two approaches may well be inspiring, especially for the future development of organic food systems. Best use of human, social and natural capital characterizes organic farmers, especially in developing countries, as documented by many case studies from sub-Saharan Africa. That also applies to organic farms in temperate zones, although usually more external inputs are used in organic farming there. While the profitability of organic farms is comparable to or slightly higher than that of conventional ones, per area food production is lower by an average of 20 to 25 percent in temperate zones. Overly restrictive production standards are often mentioned as the cause, but also a lag in production techniques. One of the main approaches of organic agriculture to augment productivity is ecological or eco-functional intensification. Thereby, the goal is to maintain the ecological and social qualities of the farms and to increase food output. The future development of organic agriculture can be characterized by a comprehensive culture of innovation embracing social, ecological and technological innovations. Such a concept of innovation includes dynamic interactions between farmers and scientists in order to strengthen system resilience and make better use of basic research from a wide range of scientific disciplines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".