Understanding agribusiness: a history of the farm problem
Bibliographic record
Abstract
Purpose This paper aims to explore the history of a politically charged term, agribusiness. The term represents both market and government forces in an essential industry. Design/methodology/approach A history is exposed while actor-network theory and non-corporeal actant theory permit the exploration of how meaning is made and given to members of a value chain. Findings The Farm Problem of the early 20th century foretells many future bubbles as well as the tension between market-focused economics and the political need for a stable food supply. The term agribusiness came into being to infuse a business approach into agriculture but the concept (a non-corporeal actant) has morphed and spread throughout the global food and fibre value chains. Research limitations/implications The work relies on published accounts and theories which are likely incomplete. Practical implications Agribusiness has been further complicated by supply chain issues of the recent pandemic. By reviewing the origins of the idea in the Dust Bowl, the New Deal, two World Wars and the interwar and post-war periods policymakers and practitioners may foresee upcoming crises. Social implications Food (along with shelter and safety) are the fundamental needs of humans. Understanding how food is produced and supplied are key to the continuance of society. Originality/value Non-Corporeal Actant Theory (NCAT) provides a unique means of exploring the role of people, places, things and ideas in the history of industries and economies. The history of farming is a challenging mix of government, trade and markets which requires a robust method of enquiry embracing complexity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".