The role of information and communication technologies on moral agents and governance in society
Bibliographic record
Abstract
Purpose This paper aims to examine the role of information and communication technologies (ICT) on moral agents, and in turn, governance structures in western societies. Design/methodology/approach This conceptual paper takes a holistic approach to governance and recasts popular notions of e‐governance by answering fundamental questions about the potential roles of governance in individuals, communities, organizations, governments and society. Findings The authors argue that it is only when the context of the moral agent is fully understood that it is possible to begin to unravel whether ICT is likely to have beneficial or detrimental effects on fundamental governance goals. Research limitations/implications Future research into e‐governance topics would be well served by discussing the governance goal that ICT is designed to improve or enhance. Whether ICT can make aspects of e‐government quicker and faster is not in dispute; however, whether ICT will actually achieve deeper governance goals requires reframing research questions. Social implications When viewed as moral agents, individuals, communities, organizations, governments and societies can use governance goals to enhance both self‐actualization and social order in line with community values. Originality/value By recasting the question “What can ICT contribute to governance and government?” to “How will ICT affect governance?”, we move away from the presumption of a positive influence, and suggest that contributions to governance goals should guide our discussions surrounding ICT utility.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".