Does Federalism Prevent Democratic Accountability? Assigning Responsibility for Rates of COVID-19 Testing
Bibliographic record
Abstract
Does federalism prevent citizens from holding governments accountable for their actions? The pandemic represents the ideal scenario for testing the effects of federalism on democratic accountability because citizens are highly motivated to hold governments accountable for preventing or failing to prevent the rapid transmission of the virus. Previous research suggests that a number of institutional and political factors complicate the accountability function in federal systems. We add to this literature by assessing the effect of one political factor, exclusivity (measured in terms of policy variation at one level), on accountability. The coronavirus pandemic provides a unique opportunity to assess this factor given the high levels of issue saliency, media attention, and low levels of intergovernmental and interparty conflict it has generated. Drawing on original data from the May 2020 Democratic Checkup Survey and public data from the Canadian National Microbiology Laboratory, our preliminary findings suggest that interprovincial policy variation with respect to coronavirus testing is not correlated with public assessments of the adequacy of provincial testing, and so it seems that Canadians are not able to assign responsibility to the correct level of government despite ideal conditions for doing so.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".