An Overview of Normative Frameworks for the Protection of Development-Induced IDPs in Kenya
Bibliographic record
Abstract
Abstract Based on the assumption that development induced displacement brings new challenges that the existing protection frameworks may not be aptly suited to deal with, this article analyses how the existing laws have met this challenge and the prospects for further improvement. While its focus is on Kenya, it evaluates the normative quality of protection and standards offered by regional instruments against the existing, as well emerging, parameters for implementation at the domestic level. In this regard, the article examines the propriety of Kenya’s newly promulgated law on internal displacement in providing for protection for the development induced IDPs, the implementation programme that it establishes and its prospects for furthering the vision of the UN Guiding Principles on Internally Displaced and other regional instruments.
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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 | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| 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".