Capacity development and reconstruction in post‐conflict African environments
Bibliographic record
Abstract
Abstract There have recently been concerted efforts by many post‐conflict African countries to formulate and implement policies and measures that will reconstruct and develop their societies. Much of the discussions of realizing post‐conflict reconstruction and development have generally focused on disarmament, demobilisation and reintegration (DDR) of ex‐combatants. What is however, missing is a discussion on capacity development and capacity building initiatives to help in reconstruction in the period after DDR. This paper therefore examines the importance of capacity development in post‐conflict African environment. It notes that while demobilising and disarming warring factions is important, the success of reconstruction efforts in a post‐conflict environment depends largely on the ability to build and develop capacity and skills that are pertinent to helping reconstruct and promote the development goals of the countries. It is argued that post‐conflict societies should have a coherent and co‐ordinate approach to rebuilding, reconstructing and developing the capacity of the state in order to achieve the state's legitimacy and effectiveness. Such capacity development measures should involve the development of physical infrastructure; the building of the state's institutional structures; the promotion of good political and economic governance; skills and education training for individuals; and measures to improve and deliver security and social services.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".