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Record W4406645655 · doi:10.5539/ibr.v18n1p14

Project Governance Practices: Influence on the Appropriation and Sustainability of the Values of Infrastructure Projects

2025· article· en· W4406645655 on OpenAlexvenueno aff
Victor Mignenan, Serge Monglengar Nandingar, Eric Drocky Bayock

Bibliographic record

VenueInternational Business Research · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAppropriationBusinessCorporate governanceSustainabilityEnvironmental resource managementEnvironmental economicsFinanceEconomics

Abstract

fetched live from OpenAlex

Project governance practices play a central role in the performance and success of initiatives, yet their contribution to the appropriation and sustainability of values remains underexplored, particularly in the infrastructure sector. This research, focused on La Grande Alliance (LGA) in the Baie-James region, investigates the effectiveness of governance practices through a mixed-methods approach combining semi-structured interviews, evaluation reports, and stakeholder surveys. The findings highlight the critical role of participative management at every stage of the process. During the definition of values, regular consultation, delegation of responsibilities, transparency, and decentralized leadership foster their appropriation. During the awareness and integration phases, collaborative leadership strengthens stakeholder engagement. Finally, in the monitoring and evaluation phase, ethical and collaborative decision-making ensures not only the appropriation of values but also their sustainability, encompassing aspects such as durability, socio-economic impact, and ecosystem protection. In conclusion, effective governance practices establish a participatory and coherent structure, enabling stakeholders to appropriate project outcomes while ensuring the long-term benefits of infrastructure initiatives in the region.

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)0.005
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
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 head0.107
GPT teacher head0.462
Teacher spread0.355 · 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.

Study designObservational
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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