Does Kenya’s Development-Induced Displacement, and Resettlement Policy Match International Standards? A Gap Analysis and Recommendations
Bibliographic record
Abstract
Multilateral Development Finance Institutions (DFIs) apply variable Development-induced displacement and resettlement (DIDR) policies for project investment-finance extended to client countries. However, developing countries, in essence, finance their development or investment projects separately, thus the need for a DIDR policy that matches international safeguard standards. Kenya has recently enacted far-reaching improvements in its DIDR framework informed by a long history of controversies surrounding DIDR and the colonial displacement and resettlement praxis. This paper traces the development of DIDR framework in Kenya and then develops a matrix to compare the framework with international safeguards extracted from the standards of six selected multilateral DFIs. It then analyses the gaps and prescribes measures to bridge the gaps towards the international standards. The major gaps noted are lack of solid income and livelihood restoration mechanisms and inadequate tracking, supervision and monitoring for DIDR. It has also presented a discussion on the need to fast-track attainment of the international standards, particularly in this period when Kenya is embarking on ‘Vision 2030’ development blueprint which hopes to spur Kenya to “High-Income Country” status by the year 2030. Multilateral DFIs are also piloting new Environmental and Social Frameworks (ESF) with the objective of assisting individual countries scale-up their DIDR policy. They can start by supporting Kenya to bridge the gaps as well as building human and technological capacity. Policy aspects indicated in this paper will enhance DIDR outcomes for Kenya.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".