Bibliographic record
Abstract
Since the end of the Cold War, the monopoly of legitimate organized force of many African states has been eroded by a mix of rebel groups, violent extremist organizations, and self-defence militias created in response to the rise in organized violence on the continent. African Border Disorders explores the complex relationships that bind states, transnational rebels and extremist organizations, and borders on the African continent. Combining cutting edge network science with geographical analysis, the first part of the book highlights how the fluid alliances and conflicts between rebels, violent extremist organizations and states shape in large measure regional patterns of violence in Africa. The second part of the book examines the spread of Islamist violence around Lake Chad through the lens of the violent Nigerian Islamist group Boko Haram, which has evolved from a nationally-oriented militia group, to an internationally networked organization. The third part of the book explores how violent extremist organizations conceptualize state boundaries and territory and, reciprocally, how do the civil society and the state respond to the rise of transnational organizations.The book will be essential reading for all students and specialists of African politics and security studies, particularly those specializing on fragile states, sovereignty, new wars, and borders as well as governments and international organizations involved in conflict prevention and early intervention in the region.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.010 |
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".