Special issue: infrastructure delivery and project management in low-and middle-income economies
Bibliographic record
Abstract
Abstract This presentation introduces the special issue of Cadernos EBAPE.BR, focusing on the theme of infrastructure delivery and project management in low-and-middle income economies. This work highlights the rationale for the special issue and summarizes the articles published. Infrastructure projects operate in a complex environment and must handle multi-level management governance. These challenges are even more pronounced in low-and-middle income economies. Therefore, an infrastructure project management system must not only consider its internal structure but also the changes and impacts the project has on both internal and external environments. The thematic section of this special issue features four articles. The first article, presented by Carneiro (2023), takes a critical perspective on project studies with a focus on the World Bank’s role and influence. The World Bank is one of the primary funding sources for infrastructure projects and has committed to increasing investments in infrastructure from billions to trillions of US dollars. Pereira, Gomide, Machado, and Ibiapino (2023) as well as Pinto and Teixeira (2023) concentrate on Brazilian Amazon infrastructure megaprojects. Finally, Barros, Carvalho, and Brasil (2023) discuss inland waterway transportation in Brazil. This special issue aims to delve into project management studies related to the delivery of large-scale infrastructure projects, encompassing public-private governance issues, project execution, and stakeholder engagement. The four articles provide a comprehensive overview of the challenges Brazil faces in executing such projects. They all address the often-high socio-political complexity that characterizes the context surrounding infrastructure projects in low-and middle-income countries, whose ultimate objective is to create and distribute value to their citizens.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".