Efficacy and Concerns of Technical Project in Bangladesh: An Assessment of the Managing at the Top 2 (MATT2) Project
Bibliographic record
Abstract
Least-Developed Countries (LDCs) and developing countries receive various supports for their development and technical projects from developed nations and development partners. Bangladesh is no exception. Managing at the Top 2 (MATT2) is a technical development project that was designed in collaboration with the governments of the UK and Bangladesh and carried out in Bangladesh with funding of the UK. The primary aim of the project was to provide practical training for approximately 2000 top-level officials of the Bangladesh Civil Service to enhance their efficiency in developing and implementing innovative projects to deliver public services. In this paper, the purpose, process and results of the project are analyzed using a qualitative approach to understand the benefits and barriers of the project. It was found that MATT2 produced remarkable success, with 305 performance improvement projects (PIPs) were developed and implemented by the participating bureaucrats. Government employees were benefitted from practical knowledge on project preparation, skill development and the citizens were benefited from the outputs. Although the project claims 100% success in terms of implementation, projects were influenced by some bottlenecks that include unsuitability of PIPs, lower reform value, monetary motivation and selection of project area out of the participants’ jurisdiction. The authors suggest considering the intended and unintended consequences of the MATT2 to undertaking similar projects in the future. Keywords: Bangladesh; Civil servants; Development; Experiential learning; PIPs; Skill development
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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 | 0.031 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".