Understanding differences in construction project governance between developed and developing countries
Bibliographic record
Abstract
Whereas most experts recognize the substantial differences in the construction sector between developed and developing countries, very little is known about how and to what extent construction project governance actually differs between the two contexts. In order to shed light on these differences, a suitable definition of project governance must be adopted and identical variables must be assessed in developed and developing contexts. Three characteristics of temporary multi-organizations that conduct construction projects (used here as categories of analysis) help identify these differences: formal structuring, informal structuring, and the role and participation of stakeholders. Based on three case studies, a survey, and semi-directed interviews, significant differences are found in how power and authority are exercised (and leadership styles applied), in the use of informality and in the roles assumed by stakeholders. Although the analysis of such differences is often considered a diagnosis of problems to be ‘fixed’ in projects in developing countries, we believe that these differences should be read as project governance mechanisms of adaptation to different environmental conditions, and therefore key elements that need to be fully understood by professionals working in developing countries.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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 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".