Security Sector Reform and Post-conflict Peacebuilding
Bibliographic record
Abstract
Military and police forces play a crucial role in the long-term success of rebuilding efforts in post-conflict societies. Yet, while charged with the long-term task of providing a security environment conducive to rebuilding war-torn societies, internal security structures tend to lack civilian and democratic control, internal cohesion and effectiveness, and public credibility. They must be placed under democratic control and restructured and retrained to become an asset, not a liability, in the long-term peacebuilding process. External actors from other nations, regional organizations, and the United Nations can be of assistance in this process by creating a basic security environment, preventing remnants of armed groups from spoiling the fragile peacebuilding process, and by facilitating reform of the local security sector. This book offers examples and analyses by an international group of academics and practitioners with direct experiences with security sector reform programs. The case studies offer the reader a useful laboratory in which comparisons can be made and observations tested. It will be useful to policymakers interested in understanding the complexity of addressing security sector reform and civil-military relations. —W. Andy Knight, University of Alberta, Canada.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".