Bibliographic record
Abstract
Conflict early warning is supposed to identify and trigger actions to reduce the onset, duration, intensity, and effects of multiple forms of political violence. While the commitment of nations to broader conflict prevention was not universally shared in the twentieth century, the concept of conflict prevention – and by extension, conflict early warning – has acquired salience in international relations over the last 30 years. This growing engagement, coupled with advances in computing, has triggered increased investment in enhanced early warning mechanisms with increasingly sophisticated temporal and spatial dimensions. Yet, the practical operationalization of conflict prevention and conflict early warning lags behind its theoretical development for several reasons. These include, inter alia, limitations in early warning assessments; the limited availability, coverage, quality and verifiability of real-time data; complex modelling challenges emerging from endogeneity inherent in conflict processes; and, not least, an inherent lack of political will among relevant actors to act upon robust and compelling evidence of heightened risks of organized violence. The latter is the core of the so-called ‘warning-response’ gap. Despite these challenges, investments in advanced data collection and analysis techniques including machine learning, natural language processing and artificial intelligence are influencing the practice of early warning and response. This article offers a descriptive review of the form and function of conflict early warning systems over the past four decades. In the process, it provides insight into why many of these systems have yet to live up to expectations.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".