Politicians as brands in parliamentary vs. presidential systems: A cross-national comparison
Bibliographic record
Abstract
Recent research has shown that politicians, specifically candidates, are viewed by the electorate as brands (Guzmán et al., 2015; Harrison et al., 2023), and that their brand image has an effect on voting intention (Van Steenburg & Guzmán, 2019). However, in all of these cases, the research was conducted in a presidential political system, where the electorate vote directly for a politician to function as head of the government. In parliamentary systems, though, the electorate vote for members of parliament, from which the head of government is appointed either by a non-executive president or a hereditary monarch. Therefore, the electorate in these systems do not directly choose the leader of their government, which leads one to question whether a politician’s brand matters as much in a parliamentary system as it does when the electorate vote directly for the head of state. To answer that question, data were collected in two presidential systems (United States and Mexico) and two parliamentary systems (Canada and the United Kingdom) using methodology that tests a politician’s brand image. Results show that politicians’ brands help shape the affective response toward the politicians regardless of political system, and that one’s self-brand image is as important to shaping those attitudes in a parliamentary system as it is in a presidential one. This is important to political marketers working in parliamentary systems as strategies can be developed to create a politician’s brand image that is valuable for elections as well as when governing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".