Disciplining land through data: The role of agricultural technologies in farmland assetisation
Bibliographic record
Abstract
Abstract Digital agricultural technologies are promoted for increasing productivity, environmental sustainability and transparency in farming. Critical perspectives on digital agriculture are necessary to frame opportunities and challenges for agricultural communities. However, the ways in which digital agricultural technologies are contributing to land financialisation—bringing land into the global market exchange—remains unexplored. Historically, farmland has been difficult to incorporate into global markets; the complex environments of family ownership have made farms difficult to condition, discipline and control, which has deterred investors. While the outright ownership of farmland has been unappealing to investors until recently, land ownership is becoming increasingly attractive due to technological change and shifts in land management. We use a responsible research and innovation framework to examine the movements in land via digitalisation asking: Who benefits and who loses due to these processes? And what are the consequences? We bring together the agro‐food financialisation scholarship, critical data studies and responsible innovation literature to bear on an analysis of farmer interviews and content from institutional investors. Ultimately, we argue that digital technologies, through their connection with land assetisation, are fostering growing inequities with respect to land access and farmer autonomy, and thus do not presently constitute responsible innovation.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".