Investigating political marketing using mixed method: the case for campaign spending data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article is a response to calls for new research methods in the study of political marketing. We submit that the mixed method approach to studying how political parties use opinion research and political communication is underused. More specifically we believe that campaign spending data, which are commonly analyzed in electoral studies, can become a significant source of information for the study of political marketing. We summarize the availability of electoral expenditure data in 13 established democracies before using a mixed method design to study political marketing management in Canada. We seek to validate quantitative data about marketing spending activity by administering semi‐structured interviews with practitioners who held senior campaign positions in major political parties. Our preliminary look at campaign finance through a political marketing scholarship lens reveals the strengths of drawing insights from such data but also some limitations. We conclude that, as other research has posited, Canadian political parties focus more on advertising in their approach to campaigning. More broadly, we propose that students of political marketing should balance proprietary interviews with transparent, standardized, replicable and objective sources of information such as campaign spending data, and vice‐versa. Copyright © 2013 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it