Examining stakeholder participation and conflicts associated with large scale infrastructure projects: the case of Tema port expansion project, Ghana
Bibliographic record
Abstract
Balancing economic activities with socio-environmental considerations has become a global standard for the construction of large scale infrastructure projects, including ports. In this discourse, stakeholder participation and environmental and social impact assessment (ESIA) have been stressed as important tools that can help port managers to co-create values, avoid conflicts and promote inclusive growth. Drawing on qualitative research tools and stakeholder theory, this paper explores whether and to what extent local stakeholders’ inclusion has substantial influence on addressing their socio-cultural concerns and interest. This is illustrated with a case study of an ongoing port expansion project at Ghana’s largest port of Tema. The findings suggest that although the port authority conducted an ESIA and engaged local stakeholders as part of the planning process, this did not translate into preventing the loss of valuable cultural resources of the local communities. The port authority did not place ‘value’ on cultural resources of the local communities that cannot be expressed in monetary terms. Further, lack of good faith engagement with local stakeholders led to conflicts in some cases that triggered a court action and delays. The paper concludes that stakeholder participation if not applied well, can become a ‘post-political’ tool.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".