Bibliographic record
Abstract
Abstract Financial actors have demonstrated a robust and growing interest in farmland over the course of the twenty-first century. Over the past two decades financial engineers have developed a variety of investment products linked to farmland, and as investment opportunities have decreased elsewhere in the economy, financial capital has poured into the farmland-based funds. Drawing on a rich and expanding literature, this chapter explains how financialization in farmland markets shapes land control in agrarian contexts. Formatting farmland for financial purposes is a contingent and complicated process. When it is successful, however, it often institutes new property relations that enable financial actors to extract value from agricultural production and compels farmers to intensify agro-extractive practices that undermine the ecological foundations of sustainable agriculture. With the objective of enabling scholars, activists, and policymakers to undo this process, this chapter not only details how financialization contours the socioecological conditions in agrarian contexts; it also identifies the different types of investors that are involved in the financial land rush, outlines the different types of financial products they use, and describes the processes and conditions that are involved in the financial assetization of farmland.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".