Dynamic stakeholder salience mapping framework for highway route alignment decisions: China–Pakistan Economic Corridor as a case study
Bibliographic record
Abstract
Undervaluing the stakeholders’ attributes, salience, and potential to impact a project during its planning and execution may result in stakeholders’ dissatisfaction, distrust, and opposition, leading to project controversies, cost overrun, schedule delays, and even project cessation. The existing stakeholders’ management typologies due to their inherent limitations are unable to provide the project managers with an optimal and comprehensive solution. The present study proposes a framework to improve the stakeholders’ management process by a novel way of mapping stakeholders’ attribute-based salience and potential impact probability into a dynamic stakeholder relational matrix. The framework was validated through a case study conducted on a mega-highway project from China–Pakistan Economic Corridor. The data was collected through a questionnaire survey and analyzed using SPSS. Twelve stakeholder groups with 36 stakeholders were identified. Stakeholders’ salience index and stakeholders’ impact probability were computed and mapped in the stakeholders’ salience assessment matrix (SSAM). The findings revealed significant dominance of the political hierarchy, project management, and defense services in the alignment selection process. Environmentalists, community, local authority, and non-governmental organizations were found deprived of reasonable participation opportunities, and their presence is often undermined and neglected in the selection process. However, the logical stakeholders’ classification and corresponding relational and engagement strategies offered by SSAM are expected to compensate the disparity and improve transparency in the decision process. This study contributes to the existing body of knowledge by proposing a comprehensive framework that integrates stakeholders’ salience, potential impact, and relational strategy simultaneously. The framework is expected to aid project managers during crucial project decision-making stages to assess stakeholders, their participation provisos, and desired engagement approaches. The proposed framework exhibits the requisite flexibility for its application on diverse infrastructure projects with certain project-specific modifications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".