Towards complexity of agricultural sustainability assessment: Main issues and concerns
Bibliographic record
Abstract
The sustainability of agricultural systems is of paramount concern in order to ensure the survival and wellbeing of humans throughout the world. Sustainability is a complex issue involving multiple factors that fit broadly within economic, social and environmental areas. Given its complexity, this paper examines the question of how sustainability can be assessed in a way that gives a holistic picture of the separate and interrelated factors. The paper then presents a literature review, field experience and the use of complex adaptive systems to identify the issues and concerns that need to be addressed during agricultural sustainability assessment and categorizes them into in seven groups: integration of capitals; maintaining resilience, adaptation and transformation; ensuring system performance; involving stakeholders; mixing interdisciplinary views; integration of scales; and practicing good governance. Based on these issues and concerns, a set of indicators are suggested that will assist with holistic agricultural sustainability assessment in a given area.
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 | 0.031 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| 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".