Jumping on the “brand-wagon”? An Examination of Political Branding in the 2011 Canadian Federal Election
Bibliographic record
Abstract
Political branding is an increasingly prominent term in both the academic and industry realms of political communication. Yet much debate has been waging regarding its viability as a concept of study. Some scholars express concern regarding the impact on democratic discourse and voter engagement, while others question its existence beyond a trendy marketing phrase. Before such questions of impact can be explored in-depth, it is important to first determine if political branding can actually be detected and measured as a truly unique form of political communication. The question of political branding as a measurable form of political communication will be explored through the lens of the 2011 Canadian federal election. The study begins by briefly tracing the historical evolution of political communication in post-war democracies. From there, various definitions of the concept are discussed, before moving to some of political branding’s key features. A multimodal content analysis is preformed on 33 television advertisements from the three major political parties participating in the 2011 Canadian federal election in an attempt to discover if branded qualities are present in the advertising content, and if so, to what extent?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".