Bibliographic record
Abstract
The 2012 Quebec election campaign began with opposition parties claiming that factors such as corruption and false promises (among others) had made Quebecers leery of their government institutions. The time had come to clean house and get the province back on track to good governance and prosperity. In this paper, we employ new data from the Quebec component of the Comparative Provincial Election Project to examine Quebecers' outlooks toward various government institutions. How confident are Quebecers in their political parties, governments, legislatures and civil service? Is there any evidence to suggest that Quebecers' views on these specific government institutions are any different across various levels of government? And what accounts for any negativity that Quebecers may feel? More specifically, this analysis considers a variety of plausible explanations, including poor government performance, pervasive cynicism, rising levels of cognitive mobilization, the rise of post-materialist values and declining levels of interpersonal trust, just to name a few.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".