Bibliographic record
Abstract
The theory of promissory representation (Mansbridge, 2003) proposes that voters select parties based on the pledges they made during the campaign. The elected parties then fulfill their promises and at the next election, voters reward or sanction the parties based on their pledge-fulfillment record. However, a fundamental assumption of promissory representation remains to be tested. If voters use party pledges to decide which party to vote for, they need to know which party made which pledges. To test the degree of awareness of citizens to party pledges (a factor we dub pledge awareness ), we included a module in the 2019 Canadian Election Study (CES) that tasks citizens to associate correctly six pledges found in the different electoral platforms with their respective parties. We find that while citizens may not know all six pledges included in our study, nonetheless, the most frequently selected answers to our pledge awareness questions are the correct ones. We also find that party identification and the information resources at the disposal of citizens play a large role in the citizen’s capacity to succeed at this matching task. Our study indicates that respondents tend to be more aware of the pledges made by the party they identify with, and well-informed respondents are more aware of pledges made by the other parties.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".