Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».