Governments and Citizens Getting to Know Each Other? Open, Closed, and Big Data in Public Management Reform
Bibliographic record
Abstract
Citizens and governments live increasingly digital lives, leaving trails of digital data that have the potential to support unprecedented levels of mutual government–citizen understanding, and in turn, vast improvements to public policies and services. Open data and open government initiatives promise to “open up” government operations to citizens. New forms of “big data” analysis can be used by government itself to understand citizens' behavior and reveal the strengths and weaknesses of policy and service delivery. In practice, however, open data emerges as a reform development directed to a range of goals, including the stimulation of economic development, and not strictly transparency or public service improvement. Meanwhile, governments have been slow to capitalize on the potential of big data, while the largest data they do collect remain “closed” and under‐exploited within the confines of intelligence agencies. Drawing on interviews with civil servants and researchers in Canada, the United Kingdom, and the United States between 2011 and 2014, this article argues that a big data approach could offer the greatest potential as a vehicle for improving mutual government–citizen understanding, thus embodying the core tenets of Digital Era Governance, argued by some authors to be the most viable public management model for the digital age (Dunleavy, Margetts, Bastow, & Tinkler, 2005, 2006; Margetts & Dunleavy, 2013) .
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.086 |
| Scholarly communication | 0.026 | 0.050 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.011 | 0.013 |
| 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".