Democracy’s Challenges: A Comprehensive Analysis of Political Support in Quebec and Canada
Bibliographic record
Abstract
While democratic support has been studied for decades, focusing on major cross-national trends and generalizations in public opinion has produced rather elusive conclusions on the overall state of democracy and its most pressing challenges or shortcomings. This is due, in part, to the lack of in-depth data that would allow us to parse out the complexities of citizens’ opinions about their democracies. This project begins to fill this gap, through the collection of large sample surveys conducted in Quebec in 2012 and 2014 and across Canada in 2017, under the Political Communities Survey Project (PCSP), while also drawing comparative baseline data from other large-scale national and international data sources. The purpose of the analyses in this project is to use these Canadian data as a starting point to map out political support both systemically (across the political system) and systematically (through the analysis of multiple indicators, testing several competing theoretical explanations for variations in support). The primary contribution of this project is the presentation of a more finely tuned, granular approach to the holistic understanding of perceptions of the democratic political system, one that may be drawn upon in the future by researchers interested in political support as well as by those seeking to address any democratic deficits that may exist. The approach presented in this project should, over the long term, produce the kinds of conclusions necessary to generate more targeted, adaptive solutions. For instance, the analyses illustrate that the ways in which citizens perceive those in power to be performing are key in understanding waning support, that public cynicism runs deep, and that superficial performance improvements may not be enough to remedy more deep-seated negative perceptions. The findings also reveal that complex identity patterns further complicate the support problem. In other words, any efforts aimed at addressing political support will require more targeted and sophisticated response strategies, informed by studies that pay careful attention to the entire political system (on a variety of aspects, using different assessment types) as well as to perspectives that are not generalizable (from different groups, across various sub-contexts).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".