The emergence of externally active representative bureaucracy, a narrative review
Bibliographic record
Abstract
This article analyzes the evolution of governance models within public administrations as they respond to complex socio-political challenges.It emphasizes the need to enhance the legitimacy and representativeness of decision-making processes in the face of persistent issues, social fragmentation, increasing inequalities, and political polarization.The study discusses two primary models: citizen participation, which promotes a more engaged form of democracy, and representative bureaucracy, which seeks to ensure that public administration reflects socio-demographic diversity.However, these frameworks have flaws, particularly in achieving representativeness and maintaining administrative efficiency.To address these issues, the concept of "Externally Active Representative Bureaucracy" (EARB) is proposed, which involves incorporating citizens directly into administrative structures to address specific challenges while improving the legitimacy and representativeness of decisions.The article reviews 155 academic articles to explore the various dimensions and effects of representative bureaucracy and citizen participation.The objective is to illustrate that EARB provides an innovative approach to public administration that bridges bureaucratic efficiency with citizen inclusion, inviting further research into this hybrid model to enhance our understanding of the operation of modern public administrations.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".