The Power of Political Image: Justin Trudeau, Instagram, and Celebrity Politics
Bibliographic record
Abstract
This article explores dynamics of online image management and its impact on leadership in a context of digital permanent campaigning and celebrity politics in Canada. Recent studies have shown that images can play a critical role when members of the public are evaluating politicians. Specifically, voters are looking for specific qualities in political leaders, including honesty, intelligence, friendliness, sincerity, and trustworthiness, when making electoral decisions. Image management techniques can help create the impression that politicians possess these qualities. Heads of governments using social media to capture attention through impactful images or videos on an almost daily basis seems like a new norm. Specifically, this article takes interest in Justin Trudeau’s use of Instagram during the first year immediately following his election on October 19, 2015. Through a hybrid quantitative and qualitative approach, we examine how Trudeau and his party convey a specific image to voters in a context of permanent and increasingly personalized campaigning. We do so through an analysis of his Instagram feed focusing on different elements, including how he frames his governing style visually, how his personal life is used on his Instagram to support the Liberal Party of Canada’s values and ideas, and how celebrity culture codes are mobilized to discuss policy issues such as environment, youth, and technology. This analysis sheds light on the effects and implications of image management in Canada. More generally, it offers a much-needed look at image-based e-politicking and contributes to the academic literature on social media, permanent campaigning, as well as celebrity and politics in Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".