Bibliographic record
Abstract
Digital government is a technological adventure. It applies new technologies—in particular, computer-mediated communication—to the ongoing development of democratic forms of government. While the primary focus in digital government literature is on computer-mediated politics and formal governance, these technologies have wider effects. Generally, new information technologies enable new forms of control (see Beniger, 1986, for an excellent history and the general connections between information, control, and governance). The technological changes that make digital government an option alter the possibilities of governance at all levels. Driven by the declining price of computer hardware (so-called Moore’s law) sensors (e.g., cameras, RFID tags), computers and networking make it possible to find out about and to control many hithertofore uncontrolled aspects of our lives. This article considers the effect of new monitoring technologies in the broad sense introduced by McDonald (2001) as inclusive of the range of control mechanisms—personal, informal, social, market, legal, and political—that we deploy. In general, we expect technological innovation to create ethical problems. Innovations move communities from technological and social situations for which their norms are well adapted to new situations in which the fit tends to be worse (Binmore, 2004). Even seemingly small changes in technology, especially communications and monitoring technology, produce significant stress on norms. (Consider how cell phones and then cell phone cameras challenge norms governing privacy in public spaces.) Therefore, we should expect moves toward digital government to face ethical problems. This article considers problems due to a suite of monitoring and surveillance technologies that promises significant benefits but raises issues in terms of the values of control, privacy, and accountability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".