Data Governance: The Next Frontier of Digital Government Research and Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it