Reflections on the nature of digital government research: Marking the 50th anniversary of Government Information Quarterly
Bibliographic record
Abstract
Since the advent of the digital age, the transformation of government operations, policy-making, citizen engagement, and public services has fundamentally reshaped the relationships between citizens and public institutions. Digital government, as a field of study, has evolved to address the complex challenges at the intersection of technology, governance, and society. Over the past decades, Government Information Quarterly (GIQ) has played a pivotal role in documenting and shaping this evolution from basic computerization to sophisticated digital transformation initiatives. The impact of digitalization extends across all aspects of public administration, from service delivery and policy-making to citizen engagement and democratic processes. This study brings together perspectives from leading digital government scholars to examine the nature of digital government research. Through the analysis of the journal's distinctive identity and characteristics, evolution, theoretical landscape, and methodological approaches, it offers insights into how GIQ has evolved to a transdisciplinary platform that bridges theoretical foundations with practical applications while consistently addressing emerging technological challenges, fundamental public sector values, and high-value public policy goals.
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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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.042 | 0.034 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".