Bibliographic record
Abstract
The general objective of this study is to examine the operational guidelines for the adoption of PPP with the view of assessing its success in Nigeria.This is significant because infrastructure development has in recent times assumed a central importance in Nigeria's quest for social and economic stability.Dearth of infrastructure and the unavailability of adequate fund for its development are major challenges to governments.In line with the practices in other developing countries, Nigerian governments at all levels now call on private investors to come to their aid in the form of partnership for the provision of infrastructure.In order to fully reap the benefits of PPP, there are some pre-requisites or critical factors which must be present.The data for this study was generated from Focus Group Discussion and documentary sources from books, journals and online sources.Data was analyzed through the use of analytic techniques derived from qualitative research, primarily thematic analysis (Mannning and Luyt, 2011).Data analysis involved breaking up the data into manageable themes, patterns, trends and relationships (Mouton, 2005:108).Themes that emerged from the data were identified using the technique of content analysis.This article examined the critical factors that inhibit infrastructure development in the polity and analyzed the constraints to the effective implementation of PPP which include difficulty in securing credit, delay in receiving payments, and absence of effective maintenance culture, corruption and dearth of visionary leaders.It then proffers suggestions towards minimizing these 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.966 | 0.944 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".