CRITICAL ANALYSIS OF PROJECT MANAGEMENT IN A DEVELOPING COUNTRY - THE CASE OF THE GAMBIA
Bibliographic record
Abstract
A good well maintained infrastructure network is the cornerstone of a country's economic development. It is in recognition of this fact that many Sub-Sahara Africa (SSA) countries (including the Gambia), have in recent years been investing massively in infrastructure projects. Many of these projects have however not achieved their originally stated goals of timely completion, within budget, and full compliance with the required specifications.\n\nThis study critically reviews how infrastructure projects are being managed in the Gambia, using two recently completed projects at the Banjul International Airport as case studies. The study proceeds from a general review of project management and the diverse management techniques being advocated by various literature on the subject. A 'best practice' framework is then developed using theoretical as well as real life project cases such as the L.B. Pearson International Airport project in Toronto, Canada. A detailed description of the two project case studies are then made and subsequently evaluated using the framework.\n\nThe conclusions drawn from evaluating the two cases are that the best practice framework developed from successful projects in developed western countries might not be totally applicable to a developing country like the Gambia due to profound cultural, literacy, and standard of living differentials. In addition, the importance of project planning is usually underestimated in developing countries.\n\nThe study concludes by recommending amongst other things that research and data collation on project implementation in developing countries be enhanced so that best practices, specific to developing countries, could be developed.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".