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Record W7117316631 · doi:10.1061/jladah.ladr-1320

Governance Strategies for Execution of Public Building Projects

2025· article· en· W7117316631 on OpenAlexaff
Olalekan Emmanuel Akintan, Victor Adetunji Arowoiya, Melissa Chan, David Kan, Wei Yang

Bibliographic record

VenueJournal of Legal Affairs and Dispute Resolution in Engineering and Construction · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsProject governanceCorporate governanceIntegrated project deliveryTransaction costDatabase transactionProject managementGood governanceBuilding information modeling

Abstract

fetched live from OpenAlex

The execution of public building projects often faces multifaceted challenges. One of these challenges is the governance system adopted in the execution of such projects. This study seeks to assess a suitable project governance strategy for the execution of public building projects in Nigeria. The populations for the study consist of construction professionals involved in the execution of public building projects. A survey research design was adopted using census sampling techniques and the data collected were analyzed using inferential statistical tools. The study revealed that a horizontal transaction governance strategy that relates to the employment relationship and involvement of market transactions for the supply of specialist skills and services is the best governance strategy to adopt in public building project delivery. Hence, the study posited for good governance; there must be good relationship between the project sponsors and project executors. In addition, contractual governance has the greatest effect on the delivery of public building projects due to the contractual aspect with the entire lifespan and logistics of the project. Once this strategy is well incorporated into an organization’s operation, it will go a long way toward maintaining good relationships between project participants, good governance, and effective project delivery.

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 score0.754
Threshold uncertainty score0.282

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 head0.025
GPT teacher head0.294
Teacher spread0.269 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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