Digital Dilemmas: Technology, Governance, and Canadian Municipalities
Bibliographic record
Abstract
The introduction of new digital platforms into community life – including “smart-city” technologies and firms like Uber and Airbnb – has raised the service expectations of residents, created new challenges relating to data governance and privacy, and introduced cyberthreats into the daily business of municipal administration. To understand these emerging threats and opportunities, the Institute on Municipal Finance and Governance (IMFG) convened a series of three panels in the winter and spring of 2022 focusing on the platform economy, smart city technology, and cyberattacks. This paper summarizes findings from the panel discussions as well as the relevant literature, providing direction for municipal leaders hoping to chart a course for their organizations through this uncertain landscape. The key policy issues arising from the panels are explored: • The platform economy is disproportionately a municipal issue, but local governments lack access to data. Municipalities can do more to force multinational platform firms to share data in order to assess their financial impact and determine negative externalities. Relationships between private and public actors are fluid and require frequent reassessment and patience. • Data governance – who owns and controls the data -- is key, especially with the importance of data to commercial applications stemming from smart cities and the need to satisfy national and international privacy regimes. Given the public nature of smart-city projects, residents may be confused over whether it is a private or public actor collecting and controlling their data. • Cybersecurity is of utmost concern as local governments accelerate digital transformations to keep pace with the changing expectations of citizens. Yet a lack of coordinated resource-sharing among municipalities, combined with funding shortages, ensures continued vulnerability to cyberattacks.
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.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.033 | 0.036 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".