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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".