Elimination Method of Multi-Criteria Decision Analysis (MCDA): A Simple Methodological Approach for Assessing Agricultural Sustainability
Bibliographic record
Abstract
In the present world context, there is a need to assess the sustainability of agricultural systems. Various methods have been proposed to assess agricultural sustainability. Like in many other fields, Multi-Criteria Decision Analysis (MCDA) has recently been used as a methodological approach for the assessment of agricultural sustainability. In this paper, an attempt is made to apply Elimination, a MCDA method, to an agricultural sustainability assessment, and to investigate its benefits and drawbacks. This article starts by explaining the importance of agricultural sustainability. Common MCDA types are discussed, with a description of the state-of-the-art method for incorporating multi-criteria and reference values for agricultural sustainability assessment. Then, a generic description of the Elimination Method is provided, and its modeling approach is applied to a case study in coastal Bangladesh. An assessment of the results is provided, and the issues that need consideration before applying Elimination to agricultural sustainability, are examined. Whilst having some limitations, the case study shows that it is applicable for agricultural sustainability assessments and for ranking the sustainability of agricultural systems. The assessment is quick compared to other assessment methods and is shown to be helpful for agricultural sustainability assessment. It is a relatively simple and straightforward analytical tool that could be widely and easily applied. However, it is suggested that appropriate care must be taken to ensure the successful use of the Elimination Method during the assessment process.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".