Bibliographic record
Abstract
This article examines the state of the art of early warning in Africa. It looks at the definitions of early warning, considers the historical evolution of conflict early warning systems, and takes a critical look at the debate about the link or the gap between early warning and early action. To this end, it tries to answer some important questions, particularly in relation to the purpose of early warning systems (EWSs) and their limitations so as to ensure that EWSs and early warning analysts are taken for what they are, and not criticised for what they are not or cannot do. In essence, it underscores the fact that the field of conflict early warning is not a fortune-telling business; an industry aimed at predicting socio-political events. The field and its different actors and mechanisms typically serve various purposes and rely on networks and open sources as well as cooperation. At times, some actions are indeed taken and potential conflicts prevented, but these actions do not come to the attention of outside observers precisely because nothing happened. It acknowledges, however, that the field can still learn from past experiences and improve on its delivery, at the level of both analysis and the ensuing action.
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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".