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Social Issues of Trust and Digital Government

2007· book-chapter· en· W151055159 on OpenAlexaff
Stephen Marsh, A. S. Patrick

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsBusinessGovernment (linguistics)Database transactionScrutinyContext (archaeology)Personally identifiable informationInternet privacyPublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0100.044
Scholarly communication0.0120.012
Open science0.0010.009
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.027
GPT teacher head0.297
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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