Automating the Public Sector and Organizing Accountabilities
Bibliographic record
Abstract
In this paper we examine the ways in which implementing new information and communication technologies (ICTs) to automate public sector processes affects accountability. New technologies alter conventional modes of behavior in the public sector, shedding light on certain areas of bureaucratic practice and obscuring others, and in doing so they enhance accountability and exacerbate dysfunctions. To investigate how ICTs influence the accountability equation, we explore a range of empirically documented e-government implementations, from simple transactions involving low-levels of automation to highly automated systems such as fingerprint analysis technologies. Drawing on these empirical examples, we develop a tentative framework of ICT-exacerbated accountability dysfunctions. Following this, we then discuss potential accountability arrangements for different types of e-government processes, in hope of realizing the benefits of new technologies while minimizing the potential for unaccountability and dysfunction that could arise from their application. Throughout, we stress the necessity of striking a balance between the potential benefits of ICTs to the bureaucratic process and systems that may reduce efficiency but uphold accountability.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".