Implementing Public Policies with User Fees that Align with Partisan Ideology: A Canadian Example
Bibliographic record
Abstract
The Canadian federal government not only provides public services such as infrastructure, healthcare and education that benefit all citizens, but government also provides services on an individual basis to citizens. Through a case study, this paper explores how government makes decisions that support its political party’s ideology in deciding whether or not to implement user fees for services that benefit individuals. Using public choice theory, we discuss three actors with each actor striving to maximize their utility: elected officials by obtaining enough votes to form government; citizen-voters by obtaining more benefits than what they finance through general taxation; and pressure groups by spending resources on political activities to secure the group members’ preferences. We then apply these three actors to a case study: the decision to increase user fees for criminal record suspensions. The case study brings forth an example of government not acquiescing to the majority of citizen-voters’ and pressure groups’ demands if these demands do not align with government’s self-interest such as furthering their ideological stance.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".