0374 Multi-criteria decision analysis (mcda) comparing agricultural production methods: protocol for analysing british columbia (bc) blueberries and ecuador bananas
Bibliographic record
Abstract
Background Expansion of agro-industrial approaches has raised concerns over occupational and environmental exposures, for example through intensive agrochemical use. Agricultural production decisions are influenced by assumptions regarding unit-specific criteria of ‘productive efficiency’ (revenues and yields), with limited attention to the association between costs and consequences and broader health determinants (sustainability and health effects). This study applies a population health perspective to investigate the occupational and environmental consequences of production options by incorporating a comprehensive range of criteria. Specifically we investigate, in partnership with producers: what is the ”best” agricultural production method for producing bananas in Ecuador and blueberries in BC? Methods Two MCDAs per jurisdiction (Ecuador and BC) are used to calculate aggregate scores to rank production methods (agro-industrial, agro-ecological, and mixed-methods). The first MCDA is an ‘actual’ model, representing real-world decisions (constrained producer choices). The second MCDA is a ‘preference’ model representing no constraints (producer preferences). Additionally, discrete choice modelling is used to simulate hypothetical scenarios of components (e.g. policy instruments) that would sway producers towards their preferences, with sensitivity analyses to consider the implications. Results If agro-industrial production is not the highest rank, a case can be made for more sustainable agriculture. The sensitivity of how decisions could move towards sustainable solutions that produce less health consequences and policies to facilitate such pursuit are assessed. Conclusions As producers express greater concern for sustainability and certification that recognise that ”good practices” are applied, MCDA suggests a way that evidence can be collected and analysed to support decision-making, transparently and comprehensively.
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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 | 0.043 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.123 | 0.011 |
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".