Political Dynamics and Infrastructure Allocation in the Canadian Context: A Case Study of Québec’s COVID-19 Recovery Plan (Bill 66)
Bibliographic record
Abstract
During the COVID-19 pandemic, Québec’s government tabled an economic recovery plan, Bill 66, which aimed to fast-track infrastructure projects. This article examines infrastructure spending in Québec to shed light on the role of electoral considerations in project allocation during the COVID-19 pandemic. A notable feature is that the projects’ list was made public, allowing for geospatial analysis and association with the province’s 125 electoral districts. The Canadian context, characterized by its Westminster-style, first-past-the-post electoral system, offers a unique vantage point. The article aligns with well-established political science theories on government spending allocation and underscores the significance of competitive districts, especially in transportation projects. In the landscape of pandemic-induced recovery plans, this research uncovers how a first-term government navigates the political landscape, offering invaluable insights into the Canadian political arena.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".