Development of livelihood vulnerability indicators in the context of compulsory land acquisition for infrastructure development
Bibliographic record
Abstract
Abstract Compulsory land acquisition, while essential for infrastructure development, often disrupts the social, economic, and environmental systems of affected communities. Legal reforms have ensured fair compensation and provided livelihood restoration assistance, yet implementation frequently prioritizes short-term recovery over long-term resilience due to limited understanding of community vulnerability. This study develops context-sensitive indicators to assess livelihood vulnerability in the setting of compulsory land acquisition. Using the Fuzzy Delphi Method (FDM), expert knowledge was engaged to identify and validate multidimensional indicators. From 53 indicators derived through literature review, 34 experts, including academics, practitioners, and community facilitators, evaluated their relevance. The process resulted in 27 selected indicators considered as “important”, with four emerging as particular critical: occupational vulnerability, land-related disruption, food security, and market stability. These indicators provide a practical foundation for designing targeted and equitable livelihood restoration programs. By aligning these indicators, restoration efforts can be tailored according to the vulnerability trait of the communities. It also serves as a practical and adaptable tool for equitable livelihood restoration in diverse global contexts, especially in countries experiencing increased land acquisition pressures due to rapid infrastructure development.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".