‘Land Grabbing’ in an Autocracy and a Multi-Party Democracy: China and India Compared
Bibliographic record
Abstract
Both China and India have witnessed extensive land expropriation by the states from farmers for use in industrialization and urbanization projects. Land conflicts have ensued from these developments. This paper poses two questions: 1) Why do we see a similar escalation of land dispossession in both countries, despite their distinctively dissimilar political systems, one being a one-party authoritarian regime, the other being a multi-party democracy? 2) How does the different regime type affect the politics of dispossession? Despite their diverse political institutions, government officials have been given similar incentives to chase growth by developing land, but the institutions create diverging environment for aggrieved citizens to mobilize their collective actions. While it is unsurprising that the interests of the poor and weak are not protected in an autocracy, democracy provides no automatic safety valve in defending marginalized citizens either.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".