Data Governance: The Next Frontier of Digital Government Research and Practice
Bibliographic record
Abstract
Picking up on a global orthodoxy calling for digital government transformation, governments across Canada are now introducing ambitious service reforms and broader changes to the organization and culture of public service institutions. These reforms are primarily justified on the grounds that they are necessary if governments wish to meet the expectations of citizens accustomed to the innovative digital service offerings of the private sector. Yet with digital transformation agendas come notable changes to the ways that public sector data is collected, applied, and shared across the state and amongst private firms. These data governance reforms may prove unacceptable to citizens where they lead to privacy breaches, betray principles of equity, transparency and procedural fairness, and loosen democratic controls over public spaces and services. This chapter presents three cases that illustrate the data governance dilemmas accompanying contemporary digital government reforms. The chapter next outlines a research and policy agenda that will illuminate and help resolve these dilemmas moving forward, with a view to ensuring that digital era public management reforms bolster, rather than erode, Canadians’ already precarious levels of trust in government.
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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 | 0.115 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.014 | 0.121 |
| Scholarly communication | 0.044 | 0.050 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".