Housing, Land and Property Rights as War-Financing Commodities: A Typology with Lessons from Darfur, Colombia and Syria
Bibliographic record
Abstract
The ongoing use of landscape-based conflict commodities — diamonds and other minerals, timber, wildlife, etc. — to finance wars continues to evolve. The success with which such commodities can be transacted to support militaries, militias and insurgencies has led belligerents to innovate with additional commodities. Housing, land and property (HLP) rights within war zones have belatedly joined the list of conflict commodities that are subject to transaction, and to such an extent as to warrant significant concern. However, the use of ‘conflict HLP rights’ has not yet been operationally described in the way that other conflict commodities have been. This is a necessary first step towards deriving and designing countermeasures. This article makes a preliminary attempt to delineate the exploitation of conflict HLP rights by examining how they are transacted to support belligerent groups in three conflicts: Darfur, Colombia and Syria.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".