COVID-19, Digitization, and the "New Normal" for Municipal Government: A Study of Three Ontario Cities
Bibliographic record
Abstract
The COVID-19 pandemic is viewed as both an unprecedented challenge and an impetus for digital transformation. During the pandemic, a “new normal” discourse emerged predicting a surge in digitization that would radically and permanently change organizations. This paper examines how the pandemic has affected municipal governments through case studies of the City of Windsor, City of Kitchener, and City of Burlington. It compares how each city adapted to the pandemic through digitization and investigates if such changes have transformed citizen participation and governance in the cities under study. The paper focuses on two ways citizens engage with local government: voting in municipal elections and delegating to councils and committees. The paper aims to understand how municipalities facilitated citizen participation during a period of public health guidelines in the province of Ontario which restricted many in-person activities. It finds that digitization was limited in its extent and scope and identifies resource, security, and accessibility considerations as primary barriers to the adoption of digital technologies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".