Political marketing : a comparative perspective
Bibliographic record
Abstract
1. Introduction: Rethinking political party behaviour - Darren G. Lilleker & Jennifer Lees-Marshment 2. Political marketing in the UK: A positive start but an uncertain future - Jennifer Lees-Marshment and Darren G. Lilleker 3. American political marketing: George W. Bush and the Republican Party - Jonathan Knuckey and Jennifer Lees-Marshment 4. Canadian political parties: Market-oriented or ideological slagbrains? - Alex Marland 5. Marketing the message or the messenger? The New Zealand Labour Party 1990-2003 - Chris Rudd 6. Political marketing in Irish politics:The case of Sinn Fein - Sean McGough 7. Political marketing in Germany: The case of the SPD - Charles Lees 8. The rise andfall of populism in Austria: A political marketing perspective - Andreas Lederer, Fritz Plasser and Christian Scheucher 9. Change to win? The 2002 general election PT marketing strategy in Brazil - Josiane Cotrim Macieira 10. The re-launch of the APRA Party: The use of political marketing in Peru in a new political era - Pedro Patron Galindo 11. Scottish political marketing in a devolved system - Declan Bannon and Robbie Mochrie 12. Conclusion: Towards a comparative model of party marketing - Darren G. Lilleker & Jennifer Lees-Marshment -- .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 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".