A Digital Advance for Housing, Land and Property Restitution in War-Affected States: Leveraging Smart Migration
Bibliographic record
Abstract
The large-scale restitution of housing, land and property (HLP) for those dislocated due to armed conflict has significant repercussions for the prospect of return, recovery and durable peace. Failure to adequately engage in restitution and other remedies for displaced populations has demonstrated that the grievances generated usually do not abate, but instead grow, including over generations, to produce subsequent problems, including armed conflict. While advances in transitional justice have supported the development of mass claims processes for HLP in war-affected countries, the current magnitude and complexity of forced dislocation is beyond the ability of conventional techniques to manage in an effective and timely way. This article argues that the current approach for handling massive numbers of HLP claims in postwar scenarios needs a critical upgrade; and describes what such an upgrade could comprise with a set of advanced techniques. These techniques focus on the issues of time, the size and complexity of the problem, new spatial technologies, and the now much greater agency possessed by displaced populations made possible by mobile digital technologies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".