Picture That: Canada’s 2015 Federal Campaign Through Instagram Images
Bibliographic record
Abstract
Social media is changing the landscape of elections. It opens a new sphere for politicians and political parties to connect with citizens. Now more than ever before we are seeing our political leaders turning to social networking sites in order to campaign and disseminate information, and the Canadian 2015 federal election was a prime example of this. All three major party leaders took to social media as a campaign tactic, but how these leaders make use of social media images has gone relatively unexamined. In this research study I ask what are the common theme(s) evident in all three major party leaders’ Instagram feeds during the 2015 election campaign? And a sub-question derived from this asks: what sorts of latent campaign tactics are suggested by these themes? In order to answer these questions I use a mixed-method approach, both a visual content analysis and discourse analysis are employed using a small sample extrapolated from Instagram. In summation, two major themes are apparent in all three leaders’ Instagram pages: The Crowd Pleaser and The Family Man, both of which have underlining political agendas.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".