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Record W4409430986 · doi:10.1680/jtran.24.00115

Review of literature on funding mechanisms in Sub-Saharan Africa on road construction and operation

2025· article· en· W4409430986 on OpenAlexaff
Harry Evdorides, Abubakar Kori Alkali, Mohammed Hamza Momade

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Transport · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsDurham College
Fundersnot available
KeywordsRoad constructionBusinessEnvironmental planningRegional scienceTransport engineeringGeographyEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.221
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - TransportSame topicPublic-Private Partnership ProjectsFrench-language works237,207