Bibliographic record
Abstract
In this section the International Journal of Public Opinion Research reviews articles that have recently been published in peer-refereed journals and which broadly relate to the field of public opinion. The intention is not to give an exhaustive overview of a given study but rather to alert our readers to interesting ideas and research in our field. Blais and Gélineau use the 1997 Canadian federal election panel study to explore the relationship between supporting the winning side in an election and satisfaction with democracy. While it is well established that winners tend to have higher levels of satisfaction than losers, less research has been done to determine whether it is the election result in itself that causes this difference in satisfaction. The authors theorize that in a parliamentary system voters might gain different utility from winning at the local and national levels, and that their expectations as well as the result as such may also have an impact. Their analysis finds that winning or losing has a significant effect on satisfaction even after controlling for pre-election satisfaction levels, and that both local and national results have an effect, but expectations about the outcome of the election do not emerge as a significant factor.
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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.042 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".