Bibliographic record
Abstract
Land grabbing per se is not a new phenomenon, given its historical precedents in the eras of imperialism. However, the character, scale, pace, orientation and key drivers of the recent wave of land grabs is a distinct historical event closely tied to the changing dynamics of the global agri-food, feed and fuel complex. Land grabbing is facilitated by ever greater flows of capital, goods, and ideas across borders, and these flows occur through axes of power that are far more polycentric than the North-South imperialist tradition. Land grabs occur in the context of changes in the character of the global food regime, formerly anchored by North Atlantic empires; the integrated food-energy complex seems to be headed towards multiple centres of power, especially with the rise of the BRICS and the proliferation of middle income countries participating in many of the land transactions. Land Grabbing and Global Governance offers insights from leading scholars and experts on contemporary land grabs. This volume examines land grabs in direct relation to a global economy undergoing profound change and the role of new configurations of actors and power in governance institutions and practices.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".