Governing the Global Land Grab: What role for Gender in the Voluntary Guidelines and the Principles for Responsible Investment?
Bibliographic record
Abstract
The heightened interest in large-scale foreign agricultural investment in regions with ‘unused’ arable land has triggered a great deal of international attention. Concerns about ‘land grabbing’ have initiated efforts at the global level to establish standards for ‘responsible investment’ and good governance. These initiatives warrant critical examination given the social, political, and economic inequalities to which they are designed to respond, yet the scholarship on these initiatives frequently fails to incorporate gendered analyses. This article argues that gendered analysis of the governance of land grabs not only belongs at the local level—where it continues to yield important insights into how gender inequality is manifested in various forms of local governance—but that it is sorely needed at the global level as well. As such, this article begins an assessment of these governance frameworks and how they consider local realities, with particular attention to gender-based inequalities.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.053 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".