Data, Dialogue, and Innovation: Opportunities and Challenges for “Open Government” in Canada
Bibliographic record
Abstract
In a rapidly evolving online environment where the inter-relationship between information and innovation is evolving from primarily closed and inward structures to much more open and networked governance arrangements, the public sector faces growing pressures and new opportunities to reform and adapt. Open data and big data are now widely embraced initiatives to spur innovation both inside of and outside of the public sector. Their capacity to foster innovation is nonetheless shaped by critical tensions between traditional government structures and culture on the one hand and more open and participative notions of governance on the other hand. Within such a context, this article examines the current Government of Canada Open Government Action Plan and its three main dimensions: information, data, and dialogue. The analysis reveals that despite some progress in the realm of open data, information and dialogue are constrained by the aforementioned tensions and the need for wider reforms to various architectural facets of the public sector – administratively, technologically, politically, and socially. Across each of these layers, we consider the sorts of wider reforms required in order to facilitate systemic innovation within the government and across sectors.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.043 | 0.029 |
| Scholarly communication | 0.035 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".