Re-Thinking the Role of Compensation in Urban Land Acquisition: Empirical Evidence from South Asia
Bibliographic record
Abstract
Planned efforts to relocate human populations often entail protracted struggles over the terms on which local populations may be compensated for the loss of land, assets and livelihoods. In many instances, compensation has been established on the basis of historical market value, which in effect excludes stakeholders (e.g., encroachers, landless laborers, sharecroppers, etc.) whose livelihoods are adversely affected by land acquisition. Establishing ways of recognizing and compensating the loss of informal land and livelihood is therefore a pressing policy priority. This paper explores the challenge of compensating losses incurred as a result of rapid urban land acquisition in the Indian State of West Bengal. Drawing upon 6 months of empirical field research, it explores (1) the ways in which national and local development authorities have structured processes of land acquisition in areas surrounding Kolkata; (2) the rights and entitlements that have been used in compensating losses incurred as a result of land acquisition; (3) the degree to which local populations have been incorporated into this process; and (4) the extent to which public policy may be used in strengthening the rights of vulnerable populations to basic forms of entitlement, such as housing, employment, and social assistance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".