Indicators of Complexity and Over-Complexification in Global Food Systems
Bibliographic record
Abstract
Global food systems have increased in complexity significantly since the mid-twentieth century, through such innovations as mechanization, irrigation, genetic modification, and the globalization of supply chains. While complexification can be an effective problem-solving strategy, over-complexification can cause environmental degradation and lead systems to become increasingly dependent on external subsidies and vulnerable to collapse. Here, we explore a wide array of evidence of complexification and over-complexification in contemporary global food systems, drawing on data from the Food and Agriculture Organization and elsewhere. We find that food systems in developed, emerging, and least developed countries have all followed a trajectory of complexification, but that return on investments for energy and other food system inputs have significantly declined—a key indicator of over-complexification. Food systems in developed countries are further along in the process of over-complexification than least developed and emerging countries. Recent agricultural developments, specifically the introduction of genetically modified crops, have not altered this trend or improved return on investments for inputs into food systems. Similarly, emerging innovations belonging to the “digital agricultural revolution” are likewise accompanied by energy demands that may further exacerbate over-complexification. To reverse over-complexification, we discuss strategies including innovation by subtraction, agroecology, and disruptive technology.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".