Fires as collateral or means of war: challenges of environmental peacebuilding in the Kurdistan Region of Iraq
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental peacebuilding broadly refers to how sustainable management of natural resources can support prevention, mitigation, and resolution of conflict, as well as recovery after conflict. Shared natural resources constitute a common environmental challenge around which cooperation may be fostered. Environmentally damaging fires in conflict areas have received little attention from the peacebuilding field, especially compared to conflict related to water and oil, despite research that suggests fires may be caused or worsened by armed conflicts. The purpose of our study was twofold: (1) to investigate co-occurrences of armed conflict and fire in the Kurdistan Region of Iraq (KRI), which has seen substantial increases in both fire events and armed conflict in the past decade, and (2) to consider how the environmental peacebuilding framework could apply in this context, potentially offering a shift from conflict to cooperation around mutual environmental issues. Using data for 2016–2022, we analyzed the spatial patterns of fire/burned areas and armed conflict, considering potential connections between the two. Our findings indicated that one-fourth of the conflict hotspot areas coincided with fire hotspots. Two areas stood out as hotspots of both conflict and fire: the Amedi area in the north, dominated by the conflict between Turkey and the Kurdistan Workers’ Party, and the Makhmur area in the south, dominated by the conflict with the Islamic State. Though fires should be seen as a transboundary issue, few peacebuilding initiatives around fire and land resources are found in this conflict-ridden region, indicating a need for a long-term peace ecology approach to overcome the consequences of structural inequalities, conflict, and environmental destruction.
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.
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 | 0.000 |
| 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 | 0.001 |
| 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 it