Project Governance Practices: Influence on the Appropriation and Sustainability of the Values of Infrastructure Projects
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".