Digital Distrust: Assuring Security and Trust in Egovernment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As we enter the Anthropocene for digital information, governments are constantly seeking new ways to ‘plug-in’ populations and promote ease of access of government services. Dubbed ‘e-governance’, this concept uses Information and Communicative Technologies (ICT) to create and expand e-channels of service access to populations through the transformation and improvement of technology (Bannister & Connolly 2012). In doing so, however, the ability for government to connect with populations poses both technical and normative challenges surrounding assurance, security, and trust. Although the Government of Canada, for example, states explicitly that encryption and secure-sending of data should provide citizens with an adequate assurance of protection, this relationship is dependent upon the trust of the citizenship it serves (Immigration and Citizenship Canada 2018). What should happen, however, if the government is seeking to provide this service to a group with which it is not perceived to have a fully-established trust relationship with? Can the government ‘create’ trust through e-governance by highlighting access and transparency? This paper explores the theoretical frameworks of mutual trust and assurance which currently dictate the terms of Canadian e-government. Specifically, we explore both the normative elements of trust between marginalized groups and the government, as well as how policymakers use e-governance not only as a means of efficacy, but for explicit trust-building as well.
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.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it