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Record W6980857646

CRITICAL ANALYSIS OF PROJECT MANAGEMENT IN A DEVELOPING COUNTRY - THE CASE OF THE GAMBIA

2005· other· en· W6980857646 on OpenAlexaboutno aff

Bibliographic record

VenueNottingham ePrints (University of Nottingham) · 2005
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant-derived Lignans Synthesis and Bioactivity
Canadian institutionsnot available
Fundersnot available
KeywordsDeveloping countryProject managementCollationProject planningProject charterProgram managementInternational airportCornerstone
DOInot available

Abstract

fetched live from OpenAlex

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.251
Teacher spread0.237 · 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 designNot applicable
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

Citations0
Published2005
Admission routes1
Has abstractyes

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