Bibliographic record
Abstract
The Sierra Leone peace process following the country's 1991–2002 civil war between the Government of Sierra Leone (GoSL) and the Revolutionary United Front (RUF) benefited immensely from the energetic involvement of an eclectic mix of donors. Traditional inter-governmental financial institutions and relatively new private funding foundations all channelled aid money and technical expertise through various multilateral and bilateral mechanisms for rebuilding the country. Without a doubt Sierra Leone's recovery from the debilitating 11-year war would not have come to pass in the time and manner it did without the robust intervention of such funders. Based on fieldwork conducted in Sierra Leone at the height of the peacebuilding process, this article outlines the role played by international donors in rebuilding post-war Sierra Leone. It posits that understanding the modus operandi of the assorted mix of donor agencies in creating the country's aid architecture is key to grasping the nuances of the Sierra Leone peace process. This is partly because the aid largesse, though well intentioned, was injected into a barely functioning system that lacked a coherent recipient regime. The article concludes that though donors were critical to Sierra Leone's rebuilding efforts, their lack of emphasis on a needs-centred funding mechanism created an inadequate model to address the country's complex post-war reconstruction challenges.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".