Agrarian Platform Capitalism: Digital Rentiership Comes to Farming
Bibliographic record
Abstract
Abstract With the rise of digital technologies, a political‐economic configuration recognised as “platform capitalism” has raised concerns over monopolistic tendencies, lack of accountability, expanded rentiership, workers’ precarity, and more. Existing analyses, however, show a distinctly urban bias—centring on housing, transportation, retail, and gig labour—and have yet to engage with the agrarian dimensions of this phenomenon despite considerable potential impacts on the future of farming. Here we begin the process of theorising agrarian platform capitalism, offering a typology of platforms in the agri‐food sector, and bringing together critiques of platform capitalism with the distinctive features of agrarian political economy. Our analysis identifies four prominent characteristics of agrarian platform capitalism which largely corroborate existing critiques albeit with some distinctive contours. As in other sectors, platforms intensify rentiership regarding both real estate and digital assets. Agricultural platforms also display a familiar tendency to thrive in spaces of regulatory retreat and are in some cases even endorsed by regulatory agencies, highlighting the potential for public–private platformisation. Some agricultural platform companies deploy populist rhetoric beyond established tropes of consumer welfare, latching onto farmers’ deep frustrations with the highly concentrated agribusiness sector. Efforts to reign in agrarian platform power may be further constrained by legitimising discourses of hunger relief and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".