Digital Distrust: Assuring Security and Trust in Egovernment
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.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".