Bibliographic record
Abstract
In 1992 John Zaller formulated the most influential theory of opinion formation, the Receive-Accept-Sample, or RAS, model. The theory describes conditions under which a message is received, and, if received, accepted or rejected. According to Zaller the reception of a message depends on the intensity of the message and on individuals’ general level of political awareness. And the highly aware should be more able to resist a message when the latter does not accord with their predispositions. The aim of the paper is to propose a simple and direct test of the RAS model. The study deals with the 1988 Canadian election, an election that was fiercely fought over one central issue, the Free Trade Accord with the United States. We use the 1988 Canadian Election Study campaign rolling cross-section survey, and we test Zaller’s propositions about who is most likely to receive, and then accept the parties ’ messages about the central issue of the election. Our findings provide little support for the RAS model, especially the proposition about how acceptance hinges on the interaction of predispositions and political awareness. We discuss the implications of the findings. We suggest that when an issue is hotly debated in an election campaign, the voters who receive the party messages are able to connect them to their values and predispositions.
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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 | 0.023 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 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".