Understanding differences in construction project governance between developed and developing countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it