LGAR - Fixing the land: The role of knowledge in building new models for rural development
Bibliographic record
Abstract
Over the past five years, the term “land grab” has made international headlines. First coined by activists documenting the rise in media reports about displacements caused by the sale or transfer of land, land grabbing quickly became an object of academic research and debate. Although the phenomenon of land grabbing—both as a characteristic of the contemporary global conjuncture and as a specific set of practices in particular places—has been difficult to precisely define, academics, activists, development practitioners, and policy-makers largely agree that there has been a concerted and increased rush to acquire land over the past decade. Conservative estimates suggest that large-scale land acquisitions (LSLA, as they are commonly known) have resulted in a ten- to twenty-fold increase in the amount of land changing hands annually since 2008 (over the annual average of the preceding forty years). Ongoing research suggests that investments were prompted by a combination of factors, such as the so-called global food crisis of 2007–08; concerns over land and energy scarcity; elite politics at multiple levels’ and market failures, particularly in housing and insurance, which liberated considerable capital for investment.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".