Digital Transformation Versus Westminster Traditionalism: Mindset, Mechanisms and Critical Enablers of Systemic Adaptation
Bibliographic record
Abstract
Abstract Since its inception, digital government has been a struggle between transformational aspirations and contested reforms grounded within a traditionalist governance ethos. Following a conceptual situating of such tensions, we focus on three interrelated thematic sets of digital governance reforms: i) organizational governance and enterprise architecture; ii) COVID‐19 and hybrid work arrangements; and iii) the escalating risks and complexities of cybersecurity. This article argues that the pervasiveness of traditional Westminster principles—notably information secrecy and hierarchical control—has shackled the emergence of an alternative governance ethos more aligned with digital innovation and systemic openness. In order to forge the latter, three critical enablers of systemic transformation must be embraced: first, a more collaborative and open political mindset; second, an alternative governance architecture championed by a new organizational entity; and third, the forging of a more diverse and empowered public service to strengthen digital governance adaptation and anticipatory capacities.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.089 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".