Developing a Success Rate Metric for Evaluating Compensation in Development-Driven Forced Displacement and Resettlement in Bangladesh: A Micro-Level Approach
Bibliographic record
Abstract
Despite the large number of people being displaced every year owing to development projects, particularly in developing countries, resettling and rehabilitating displaced individuals remains a major challenge. To address this challenge, compensation is used as the primary tool for development-driven forced displacement and resettlement, provided in the form of land, cash, and houses for displaced households. The effectiveness of compensation in resettling displaced individuals is crucial. Therefore, it is essential to assess the impact of compensation on the characteristics of affected households at the micro-level. While existing research has predominantly offered a macro-level perspective on compensation outcomes, the study presents a new approach for evaluating the success rate of compensation programs at a micro-level, focusing on the characteristics of the households affected by the Jamuna Multi-purpose Bridge Project in Bangladesh. The approach uses the chi-square test to assess changes in household living conditions before and after compensation. It determines if compensation has an equal or unequal effect on different household characteristics across eight categories derived from the Impoverishment Risk and Reconstruction (IRR) model. The study calculates the success rate of compensation by counting the number of equal effects for all household characteristics within each category. The findings led to the development of a success rate metric for evaluating compensation programs, where a success rate of 50% indicates moderate success and 75% indicates a successful outcome. This provides a clear and quantifiable measure for assessing the effectiveness of compensation and guiding the development of fairer compensation policies and outcomes.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".