Review of literature on funding mechanisms in Sub-Saharan Africa on road construction and operation
Bibliographic record
Abstract
Many studies on road and highways construction and operation (RHCO) have been conducted by researchers worldwide over the years, but there has been no study to date whereby the findings from the previous articles have been extensively reviewed from a geographical perspective. Furthermore, the published articles have not been unanimous in their findings for the same region, further bringing into question the reliability of previous research. The aim of this study is to review previous research on the topic of RHCO to identify the reasons for the variation in the findings from a geographical perspective. From the previous articles, the authors focused on (a) understanding available funding mechanisms, (b) benefits/challenges in funding mechanisms and (c) recommendations to improve current funding mechanisms. The review has been conducted in accordance with the standard of the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI) and follows a ‘preferred reporting items for systematic reviews and meta-analysis’ or ‘PRISMA’ model. The EPPI reviewer web software was used for data analysis and coding, studies identification, screening, eligibility check and final inclusion. The authors have prepared a cost/benefit analysis with recommendations summarizing the challenges faced in countries with poor economic conditions.
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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".