Assessment of Bangladesh Public Procurement System
Bibliographic record
Abstract
Bangladesh has enjoyed relatively high and stable growth over the last two decades,accompanied by rapid poverty reduction. Gross domestic product (GDP) growth averagedclose to 6 percent annually since 2000 and, according to official estimates, accelerated toover 8 percent in FY19. The poverty rate dropped from 44.2 percent in 1991 to 14.8 percentin 2016. With per capita gross national income (Atlas method) at $1,954 in 2019, Bangladeshhas moved into lower middle-income country status since 2015. The Government of Bangladesh (GOB)’s Vision 2021 aims to propel the country into middle-income status and further reduce poverty. The most recent five-year plan (FYP16-20) focusses on productive employment for the growing labor force and a substantial increase in investment. Other key elements of the plan are to ensure good governance and pursue for an environmentally sustainable and socially inclusive development process. The key objectives of the assessment were to: (i) establish a shared understanding of thecurrent state of Bangladesh public procurement system amongst all stakeholders; (ii) identifythe strengths and weaknesses of the overall public procurement system and formulateappropriate mitigation measures for the identified gaps; and (iii) develop action plan forfuture system development in achieving a modern and harmonized procurement system withparticular reference to enhanced e-GP, contract management, sustainable procurement, andcitizen engagement.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.012 |
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".