PUBLIC MANAGEMENT CHALLENGES IN THE DIGITAL RISK SOCIETY: A Critical Analysis of the Public Debate on Implementation of the Danish NemID
Bibliographic record
Abstract
The rise of the digital society is accompanied by incalculable social risks, but very little IS research has examined the implications of the new digital society. Drawing on concepts from Beck’s critical theory of the risk society and critical discourse analysis, this study examines the public discourse on risk events during the launch of NemID, a personal digital identifier for Danish citizens. This research illustrates our difficulties and challenges in managing some of the fundamental social risks from societal digitalisation. Limited institutional capabilities for digital technologies force public officials to depend on private companies motived by profit instead of the public interest. Beliefs in digital technology as the primary determinant of social and economic progress also present many public management dilemmas. When digital risk events occur and citizens’ fears are stoked by news media and public discourse, public officials seem to have no other strategy for managing the escalating fears than systematically distorted communication. The continued rise of the digital risk society demands that IS research respond to the challenge of generating knowledge for its public management.
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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 | 0.058 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.031 | 0.059 |
| Scholarly communication | 0.029 | 0.019 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".