Bibliographic record
Abstract
As COVID-19 swept through Canada, cities were at the front lines in curbing its spread. From March 2020, municipalities introduced such measures as restricting park access, ticketing those lingering in public places, and enforcing physical distancing requirements. Local governments have also supplemented housing for the vulnerable and given support to local “main street” businesses. Citizens expected their local governments to respond to the pandemic, but few people know how constrained the powers of municipalities are in Canadian law. Municipalities are a curious legal construct in Canadian federalism. Under the constitution, they are considered to be nothing more than “creatures of the province.” However, courts have decided in many cases that local decisions are often considered governmental and given great deference. This chapter focuses on the tensions in this contradictory role when it comes to municipal responses to COVID-19, particularly when those responses take the form of closure of public spaces, increased policing by bylaw officers, and fines. I conclude that municipalities serve an important role in pandemic responses, alongside provincial and federal governments. Provincial law should be amended to capture the important role of municipalities in Canadian federalism, especially in the area of municipal finance.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".