Political marketing: a comparative perspective
Bibliographic record
Abstract
Political marketing has become a global phenomenon as parties try to copy the market-oriented approach employed by Tony Blair to win power for New Labour in 1997. Increasingly voters choose parties like consumers choose products, and this study looks at how some political parties, such as Sinn Fein, have been able to capitalise on this to gain support. It raises fresh perspectives on the more established political marketing practices in the UK and US, such as how to incorporate political leadership within the market-oriented framework and the democratic implications when faced with the actually business of governing. This book also highlights how the market-oriented party approach has spread around the world, including Europe and the new democracies of Brazil and Peru. The chapters, in demonstrating this convergence in practices, also question whether this strategy is appropriate for political systems based on proportional representation and coalition governments such as those in Austria, Germany, New Zealand, Canada, and devolved systems in Northern Ireland and Scotland. The collection also introduces the debate on whether such practices enhance or undermine democracy, raising important questions on the future of political marketing. This book should become an established essential text for students and academics of political science and marketing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".