Bibliographic record
Abstract
Building any online system or service that people will trust is a significant challenge. For example, consumers sometimes avoid e-commerce services over fears about their security and privacy. As a result, much research has been done to determine factors that affect users’ trust of e-commerce services (e.g., Egger, 2001; Friedman, Khan, & Howe, 2000; Riegelsberger & Sasse, 2001). Building trustable e-government services, however, presents a significantly greater challenge than e-commerce services for a number of reasons. First, government services are often covered by privacy protection legislation that may not apply to commercial services, so they will be subject to a higher level of scrutiny. Second, the nature of the information involved in an e-government transaction may be more sensitive than the information involved in a commercial transaction (Adams, 1999). Third, the nature of the information receiver is different in an e-government context (Adams, 1999). Some personal information, such as supermarket spending habits, might be relatively benign in an e-commerce situation, such as a loyalty program (supermarket points, or Air Miles, for instance), but other information such as medical records would be considered very sensitive if shared amongst all government agencies. Fourth, the consequences of a breach of privacy may be much larger in an e-government context, where, for example, premature release of economic data might have a profound effect on stock markets, affecting millions of investors (National Research Council, 2002). E-government services also involve significant privacy and security challenges because the traditional trade-offs of risks and costs cannot be applied as they can in business. In business contexts it is usually impossible to reduce the risks, for example of unauthorized access to information, or loss of or corruption of personal information, to zero and managers often have to trade-off acceptable risks against increasing costs. In the e-government context, because of the nature of the information and the high publicity, no violations of security or privacy can be considered acceptable (National Research Council, 2002). Although zero risk may be impossible to achieve, it is vital to target this ideal in an e-government service. In addition, government departments are often the major source of materials used to identify and authenticate individuals. Identification documents such as driver’s licenses and passports are issued by government agencies, so any breach in the security of these agencies can lead to significant problems. Identity theft is a growing problem worldwide, and e-government services that issue identification documents must be especially vigilant to protect against identity theft (National Research Council, 2002). Another significant challenge for e-government systems is protecting the privacy of individuals who traditionally have maintained multiple identities when interacting with the government (National Research Council, 2002). Today, a driver’s license is used when operating an automobile, a tax account number is used during financial transactions, while a government health card is used when seeking health services. With the implementation and use of e-government services it becomes possible to match these separate identities in a manner that was not being done before, and this could lead to new privacy concerns.
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 | 0.000 | 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".