Political economy of planned relocation: A model of action and inaction in government responses
Bibliographic record
Abstract
Planned relocation has been shown to have significant impacts on the livelihoods and wellbeing of people and communities, whether the resettlement process is inclusive or coercive. For states, planned relocation represents risks to those communities but also to government investments and political legitimacy. Evaluations of relocations commonly focus on the risks and benefits of government interventions while overlooking the consequences of not intervening. Here we develop a conceptual framework to examine the factors that influence government decision-making about whether or not to undertake planned relocation of populations in the context of environmental change. The study examines planned relocation decisions and non-decisions by government agencies in West Bengal in India for communities seeking relocation due to coastal flooding. It focuses on three localities facing river erosion losing significant land areas in small islands and communities where populations recognize the need for public intervention, but where there has been a diversity of responses from the state authorities. Data are derived from interviews with key respondents involved in planning and implementing relocation and with residents affected by those government decisions (n = 26). These data show that government action is explained by a combination of risk aversion within political systems to avoid perceived negative consequences, and a lack of government accountability. The empirical cases demonstrate the uneven application of action and inaction and the consequent uneven distribution of potential outcomes on populations. The study suggests that while there may be a growing demand for planned relocation in places affected by environmental change, its implementation is likely to be uneven, with profound socioeconomic implications for those living in such localities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".