Bibliographic record
Abstract
Abstract In writings on public administration, the subject areas of values and ethics and of information technology ( IT ) have received substantial, but largely separate, attention. The public administration community can benefit by drawing on scholarship in the field of information and computer ethics and developing its own body of research with a view to sensitizing public servants to the effects of changes in IT on values and ethics. This article focuses on developments in the use of IT (for example, self‐service technologies, Big Data, the Internet of Things) as a basis for assessing their implications for public sector values and ethics. Research is needed on the extent to which the values and ethics regimes of public organizations take account of the impact of changes in IT ; the degree to which the various components of these regimes can foster sensitivity to the implications of these changes; and the significance for the public sector of such emerging ethical issues as robot ethics. Value conflicts and dilemmas arising from advances in digital technologies argue for vigorous measures to alert public servants to the technologies' impact.
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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.141 |
| Scholarly communication | 0.027 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".