Bureaucratic Entrepreneurship: Administrative Behavioral Changes and E-Government Advancement in Bangladesh
Bibliographic record
Abstract
This study analyzes how a postcolonial democracy like Bangladesh is experimenting with electronic or e-Government agendas, to address challenges in public service delivery traditions and processes, through soft administrative reforms.The mainstream literature on Bangladesh focuses on how the dual colonial legacy structured bureaucrats' behavior within the post-independence political context, making it unresponsive to societal needs.Taking a cue from this, the present study investigates the political, structural, and behavioral conditions which impeded successive public administration reforms overtime in Bangladesh, and analyzes the conditions which may have influenced administrative behavior for e-Government implementation.It looks at the power struggles at the higher levels of bureaucracy, and its effect on the implementation of public administration reforms.The dissertation uses a mixed-method approach: content analysis, interviews, and survey findings.An analysis of secondary literature charts out the colonial formation of the Bangladesh bureaucracy, how it endured and resisted reforms under different regimes, and how the onset of Digital Bangladesh created new political expectations of the public administration.This study demonstrates mechanisms through which ideas generated by different international models such as New Public Management (NPM), digital era governance (DEG), New Public Governance (NPG) and design thinking (DT), were applied nationally to make the Weberian-colonial bureaucracy more entrepreneurial and citizen-centric.The study explores how these models influenced the design of capacity building initiatives, some of which continued despite politicization of the bureaucracy, and set the ground for e-Government transformation under Digital Bangladesh.which exerted a major influence on my thinking about public administration.Without her encouragement
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".