Special issue: infrastructure delivery and project management in low-and middle-income economies
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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