Politicking and Visual Framing on Instagram: A Look at the Portrayal of the Leadership of Canada’s Justin Trudeau
Bibliographic record
Abstract
While digital media have become a central component of the contemporary political communication mediascape, many politicians have still not fully embraced this digital shift. However, Justin Trudeau and his team understood quickly that these platforms were powerful public outreach and engagement tools. Since his election as prime minister of Canada, Trudeau has been able to exploit the image-making and framing capabilities of digital media platforms to roll out a strategic narrative about his political leadership. Building on an analysis of all posts on his Instagram account during the year following his election as prime minister of Canada, this article is taking a close look at how Trudeau turned to visual framing in his Instagram posts to generate strategic political narratives, which emphasized and reinforced seven traits of his political leadership: 1) innovative leader at the helm of a prosperous country; 2) leader dedicated to positive policies; 3) leader dedicated to national unity; 4) leader promoting and respecting minority and marginalized communities; 5) comforting and reassuring leader; 6) leader valuing international dialogue and respect; 7) relatable leader. In doing so, this research work provides insights of interest into a specific dimension of the visual political communication strategy deployed by elected official on Instagram. More importantly, this article contributes to ongoing academic work on dynamics of visual political image-making and framing on social media in Canada and abroad.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".