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Record W4308865141 · doi:10.5267/j.msl.2022.11.001

Factors leading to failures of infrastructure development projects in Pakistan: A systematic literature review

2022· article· en· W4308865141 on OpenAlexvenueno aff
Sabahat Nisar, Rabia Asif

Bibliographic record

VenueManagement Science Letters · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCorporate governancePublic sectorFinanceAccountingRisk analysis (engineering)Economics

Abstract

fetched live from OpenAlex

Public sector development projects are found to have poor repute in terms of completion within assigned cost and time. Almost every 2nd project is impacted by financial burdens and time delays. The aim of this literature review is to highlight the potential factors that impact the completion time and estimated cost with respect to public sector infrastructure development projects in Pakistan. 39 sub-factors have been identified from 32 existing research which are then classified into 8 main factors. The main factors include administrative issues, improper financial plans, market risks, input cost estimates, poor corporate governance, legal issues, technical matters errors, and environmental issues. The results of this systematic literature review will provide policymakers with oversight on how to formulate effective strategies regarding cost cutting and timely execution of the infrastructure development projects in Pakistan.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0160.016
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.279
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations11
Published2022
Admission routes1
Has abstractyes

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