Armed Conflicts in Africa and Environmental Intelligence for Sustainability
Bibliographic record
Abstract
Armed conflicts cause considerable human and economic impacts, resulting in economic decline, social dislocation, and ecological disaster. In addition to being humanitarian disasters, armed conflicts cause considerable environmental damages to vital infrastructures and resources, some of which are irreversible or persist a long time after the end of the war, compromising potential sustainable recovery and reconstruction. Anticipation or risk of occurrence of conflicts may impair the sustainable development of involved countries if it was planned as if conflicts did not exist or would not occur. This paper introduces the notion of Environmental Intelligence for Sustainability as a tool to manage and possibly incorporate those risks within the sustainability agenda with particular emphasis in Africa. In this paper, the concept of Environmental Intelligence for sustainability (EIS) is defined as a strategic approach to analyze and manage the relationship between anticipated or on-going armed conflicts and sustainability. It may range from a pre-conflict strategic environmental and social assessment to governance and management tools developed as a three-dimensional framework operationalized through preventive, prospective and reactive measures. In view of the regional, and global effects of conflicts, coordinated Environmental Intelligence for Sustainability in African countries should be viewed by the international community as one of the main components of peace building globally, and a primary condition for sustained economic development and achievement of 2030 sustainability goals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".