Mass Claims in Land and Property Following the Arab Spring: Lessons from Yemen
Bibliographic record
Abstract
The Arab Spring uprisings have released a flood of land and property conflicts, brought about by decades of autocratic rule. Expropriations, corruption, poor performance of the rule of law, patronage and sectarian discrimination built up a wide variety of land and property transgressions over approximately 30 years. The result has been the creation of longstanding, acute grievances among large components of national populations who now seek to act on them. If new, transitional or reforming governments and their international partners fail to effectively attend to such grievances, the populations concerned may act on them in ways that detract from stability. This article critiques the case of Yemen, whose transitional government, with international support, initiated a land and property mass claims process in the South in order to address a primary grievance of the southern population as part of the National Dialogue transition. A series of techniques are described that would greatly improve the mass claims process once it inevitably recommences after the Houthi conflict comes to an end. These improvements would attach more importance to socio-political realities and how to quickly attend to them, as opposed to an over-reliance on specific legalities. Such an approach could have wider utility among Arab Spring states seeking to address similar land and property grievances.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".