One Sunshine Doth Not a Harvest Make: An Examination of the Growth Momentum in Bangladesh
Bibliographic record
Abstract
This paper revisits the issues and trends observed in M.G. Quibria’s new book (Quibria 2019) and attempts to re-examine and interpret the recent episodes of economic growth in Bangladesh, both in its quantity and, to a lesser extent, in its quality. Indeed, current estimates appear to put Bangladesh as the growth leader of South Asia. Touching the 8 per cent threshold, the recent growth pace has led to a significant reduction in poverty, a modest increase in inequality, and major advances in pertinent social and human indicators. Under what scenarios may the recent growth momentum survive and continue to unleash further growth in the quest toward reaching the higher middle-income status in the next several decades? We evaluate the task at hand in the context of innovations both in the proximate sources of growth (namely, accumulation of human and physical capital and in total factor productivity TFP) and in institutional capital. The paper also briefly touches on concerns raised in the current growth and development literature of the looming challenges of future growth slowdown as experienced by countries failing to overcome the “middle-income trap” and falling prey to “premature de-industrialisation” at income levels much lower than the early growth leaders of the past century.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".