Bibliographic record
Abstract
Previous research has shown that those who won an election are more satisfied with the way democracy works than those who lost. What is not clear, however, is whether it is the fact of winning (losing), per se, that generates (dis)satisfaction with democracy. The current study explores this winner/loser gap with the use of the 1997 Canadian federal election panel study. It makes a theoretical and methodological contribution to our understanding of the factors that foster satisfaction with democracy. At the theoretical level, we argue that voters gain different utility from winning at the constituency and national levels in a parliamentary system, and that their expectations about whether they will win or lose affect their degree of satisfaction with democracy. On the methodological front, our analysis includes a control group (non-voters) and incorporates a control for the level of satisfaction prior to the election. The results indicate that the effect of winning and losing on voters' satisfaction with democracy is significant even when controlling for ex ante satisfaction before the election takes place, and that the outcome of the election in the local constituency matters as much as the outcome of the national election. They fail to show, however, that expectations about the outcome of the election play a significant role in shaping satisfaction with democracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".