Defining “success” in large-scale agricultural investment: a typology based on different stakeholder perspectives
Bibliographic record
Abstract
Abstract In 2007/2008, a triple crisis of food, fuel and finance sparked a global rush for agricultural land; tens of millions of hectares were acquired, primarily by foreign investors, within countries in the Global South. Amidst those transactions, intergovernmental organizations, national governments, investors, and community members envisioned what “success” of such investments entails. Although not explicitly defined, each stakeholder had different conceptualizations and measures of it, based upon the descriptions used and desired outcomes sought. Despite a large amount of literature analyzing the global rush for land, as far as we are aware no one has analyzed the diverse viewpoints about what success entails. This paper compares conceptualizations among four key stakeholder groups, based on ideal types from dominant narratives, and develops a typology of ideal stakeholder framing of success to allow comparisons of uses and thereby provide a foundation for researchers who are assessing the global land rush. This paper provides clarity about widely used, but inconsistently defined, framing providing an important foundation for clarity of meaning and comparative differences between stakeholders. The typology advances the discourse on the land rush by providing nuance to this widely used framing and makes explicit its diverse meanings.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".