Introduction to a Symposium on Global Finance and the Agri‐food Sector: Risk and Regulation
Bibliographic record
Abstract
This symposium introduction brings together two debates; the debate on global food prices and speculation, and the debate on so‐called global ‘land investment’ or ‘land grabbing’. Both debates are examining two sides of the same phenomenon – the growing role of private financial investors in the global agri‐food value chains and the myriad consequences of it. The symposium moves beyond the identification of finance as an exogenous factor to the trends in the sector. It examines real‐life incarnations of finance in the sector by looking at investment arrangements, including connections with the state, and its (regional) variations. The symposium addresses three main themes. First, it explores the interplay of the state and private finance. It shows that the effect of regulation is limited in the face of increasingly mobile and complex investment flows. Second, it addresses the shifts and transfigurations of risk in the agri‐food sector due to financialization. Third, the symposium discusses to what extent, and how, the origins and identity of farmland investors still matters within an increasingly globalized financial sector. The paper concludes by identifying some related areas for further research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.034 | 0.010 |
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".