E-Government and Local Governance in Canada: An Examination of Front Line Challenges and Federal Tensions
Bibliographic record
Abstract
The purpose of this article is to examine the impacts of e-government in Canada on both inter-governmental relations and local governance. The rationale for such an examination stems from the emergence, over the past decade of two parallel discourses in public sector and governance reform: first, e-government as primarily a set of national and provincial strategies for public sector reforms, and secondly, a discourse has focused on the rising importance of municipal government and local governance systems. The main problem at present remains the absence of more holistic thinking on the need for a new enterprise, federated architecture for collaboration that entails an overhaul of the existing political arrangements of the federation. An additional lesson to draw at present is that the weak status and limited capacities of Canada's municipalities, a concern predating e-government's emergence, risk amplification as a governance handicap for both individual communities and the country in adapting to a more digital age. However, the consequences of the weakness also depend on how provincial and federal governments respond to the erosion of public trust by adapting their own structures, as well as the effectiveness of emerging top-down mechanisms being deployed to strengthen the infrastructure of cities and communities.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.034 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".