The Power of Political Image: Justin Trudeau, Instagram, and Celebrity Politics
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 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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| 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