Fires of war: how civil war shaped fire regimes in East Angola
Bibliographic record
Abstract
Research on the environmental impacts of warfare is limited and often not interdisciplinary. Of the many impacts that war can have, its effect on fire activity is particularly understudied, despite the importance of fire to livelihoods and ecosystem functioning in fire-dependent ecosystems, such as some savannas and woodlands. This article investigates the impact of the Angolan civil war on fire activity in the highlands of East Angola, an area that served as a stronghold for the “guerrilla” forces during the conflict and where local peoples have historically used fire as a livelihood tool. This study employs historical remote sensing data (derived from the National Oceanic and Atmospheric Administration’s AVHRR-LTDR satellite), and interviews to 42 elders to reconstruct wartime and post-war fire regimes. Interview data suggest that fire events were rarer during the war (1975–2002) compared with the post-war period (2003–2018), a trend corroborated by satellite-derived time-series analyses (from 1982 to 2018). We identified four main factors behind this change: limited use of fire as a warfare tool, displacement of people, strict fire governance, and changes in fire use for subsistence. This research highlights that socio-political dynamics, and particularly civil war, significantly shape fire regimes. Yet a convergence in pre-war and post-war fire patterns is identified: post-war increases in burned area may reflect a return to a pre-war baseline, underscoring the need for historically informed, interdisciplinary research to identify the most suitable fire management approaches in East Angola and beyond.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".