Open Data and Open Governance in Canada: A Critical Examination of New Opportunities and Old Tensions
Bibliographic record
Abstract
As governments develop open data strategies, such efforts reflect the advent of the Internet, the digitization of government, and the emergence of meta-data as a wider socio-economic and societal transformational. Within this context the purpose of this article is twofold. First, we seek to both situate and examine the evolution and effectiveness of open data strategies in the Canadian public sector, with a particular focus on municipal governments that have led this movement. Secondly, we delve more deeply into—if and how, open data can facilitate more open and innovative forms of governance enjoining an outward-oriented public sector (across all government levels) with an empowered and participative society. This latter vantage point includes four main and inter-related dimensions: (i) conceptualizing public value and public engagement; (ii) media relations—across traditional intermediaries and channels and new social media; (iii) political culture and the politics of privacy in an increasingly data-centric world; and (iv) federated architectures and the alignment of localized, sub-national, and national strategies and governance mechanisms. This article demonstrates how each of these dimensions includes important determinants of not only open data’s immediate impacts but also its catalytic ability to forge wider and collective innovation and more holistic governance renewal.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.051 | 0.038 |
| Scholarly communication | 0.031 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".