The Progressive Agriculture Index: Assessing the Advancement of Agri-food Systems
Bibliographic record
Abstract
Indicators and metric systems are crucial tools in efforts to reach societal objectives, and these systems are being employed increasingly in initiatives to improve the environmental, economic, and social sustainability of agri-food systems. Indicators can help clarify values and objectives, providing assessment criteria useful for tracking movement toward or away from targets. Unfortunately, the application of indicators and metrics to agricultural systems has been hindered by conflicting definitions of agricultural sustainability and progress, leading to metrics that lack a holistic consideration of social, economic, and environmental factors. To address this shortcoming, we argue for a definition of progressive agriculture that includes all three of the abovementioned factors, stressing the need for multidimensional improvements in the impact of agri-food systems. Our proposed Progressive Agriculture Index (PAI) integrates data from the U.S. Census of Agriculture, the U.S. Census, and other databases to assess nine variables at the county level for the contiguous United States. Including data from both 2007 and 2012 permits analysis of time trends along with regional and county-level trends in individual and aggregate measures of progressivity. By ranking counties within their Farm Resource Regions (as defined by the U.S. Department of Agriculture [USDA] Economic Research Service [ERS]), as well as within their Urban Influence Categories, the PAI also makes it possible to compare counties with similar socio-economic and environmental contexts. Given the important goal of improving social, economic, and environmental conditions in concert, we present this index to draw attention back to the often-neglected social facets of progressivity and thus contribute to advancing more integrated, participatory approaches to measuring progress in agri-food systems.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".