Bibliographic record
Abstract
The words - Rights, - Trust, - Human Dignity, - and even - Government - have widely varying meanings and connotations, differing across time, languages and cultures. Concepts of rights, trust, and human dignity have been examined for centuries in great depth by ethicists and other philosophers and by religious think-ers, and more recently by social scientists and, especially as related to information, by information scientists. Similarly, discussions of government are well documented in writings back to Plato and Aristotle, with investi-gations of electronic government (often referred to as e-government) dating back only to the early 1990s with the advent of the Internet and the World Wide Web. At first e-government was described in glowing, positive terms. Little, if any, attention was paid to two critical questions: 1) Will people trust e-government? and 2) How will cultural differences affect individuals‘ trust in government and their perceptions of govern-ment‘s effect of their human dignity? Examinations of trust and distrust by individuals within organizations have addressed questions of motives and intentions, expectations of behavior, protection of interests, confi-dence in accuracy and reliability of information, vulnerability, and reciprocity, among other complex topics. Establishing e-government services often requires going through several phases: 1) publish (using ICT to improve access to government information), 2) interact (broadening participation in government through 2-way communications, and 3) transact (making actual services available online; and 4) transform (fundamen-tally changing government to make it truly citizen-centric). Building and maintaining trust in e-government require developing an understanding both of the many levels of interactions where trust must be earned and of cultural differences. Another challenge in developing such a framework is that e-government, itself, is very dynamic, changing rapidly over time. Trust in content or a system available one day may not carry over when the content and/or system changes dramatically. Components of a framework must include the follow-ing dimensions: conceptual domains, cultural dimensions, information content dimensions, and system di-mensions. Of course, these dimensions must be considered within the context of rapidly changing govern-ments, ICT services, the digital divide, and other factors. This paper provides a very brief overview of some of the notions of trust and distrust, concentrating on those concerning trust as it relates to notions of power, trust in organizations, and trust in information and information systems as one part of a framework to ad-dress the question of trust in e-government. It also makes a few recommendations for how to build citizen-centric e-government to ensure information rights through a focus on human dignity, fundamental human rights, and earning trust.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".