Bibliographic record
Abstract
Abstract Administrative agencies have long been sites of technological innovation. Today, government officials worldwide are intensifying digitalization efforts to cut costs and to make bureaucratic operations more efficient. This article examines how digitalization initiatives are implemented in administrative settings, using examples from the United Kingdom (Universal Credit), Canada (Ontario’s Social Assistance Management System), and Australia (Online Compliance Initiative, a.k.a. ‘Robodebt’). It draws on qualitative research, government reports, and administrative justice literature to illustrate the dilemmas common to digital government projects. For example, digitalization both hardens and virtualizes the interface between officials and the public, while obscuring the vast amounts of human labour needed to maintain digital government initiatives. To function well, digital systems require deep integration between government databases and software. Yet, the process of digitalization is often piecemeal, continuous, and reproduces dilemmas that arise whenever new technologies are used to solve institutional problems. Consequently, the promised benefits of ‘digital by default’ initiatives are rarely realized. Digitalization accelerates a shift in relations between people and the state that administrative justice scholars must take seriously. First, scholars must reconsider the internal perspective from which administrative justice theories assess an outcome’s acceptability. Digitalization compels the development of new justice models centred on the values of system users within and outside of administrative institutions. Second, scholars must reassess administrative justice theory’s procedural focus. In digitalized settings, ‘administratively just’ decision-making processes may generate substantively unjust outcomes. These challenges must be addressed if administrative justice theories are to remain relevant in an age of algorithmically-driven decision-making.
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 | 0.043 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.077 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".