Digital photos, social media sharing, and the Office of Prime Minister Justin Trudeau
Bibliographic record
Abstract
Social media has given Canadians the opportunity to directly connect and share content with each other. Market trends show that users of new digital media prefer to consume and share visual content with celebrity or human interest themes. Finding such visual content is easier when it has been produced and distributed by others. In particular, both traditional news and social media sometimes reproduce digital photo handouts produced by the Prime Minister‘s Office (PMO). These handouts give the PMO an opportunity to feed a stream of positive visuals of Prime Minister Justin Trudeau into online Canadian media platforms. These campaign-style photos promote the prime minister but do little to educate Canadians on civic issues or government business. This creates a situation where the PMO might be diminishing the independence of social media spaces in the pursuit of political goals by reorienting these handouts towards social media-driven consumption. Where are PMO-produced digital photographs of Prime Minister Trudeau reproduced on social media and other non-social media web sites? By addressing this question, I attempt to demonstrate that by providing social media geared towards content sharing with affordable, in-demand digital photo handouts, political actors such as the Trudeau PMO use these platforms as distribution vehicles for their own positive leader-centric visuals. I hypothesize that almost all PMO digital photo handouts are reproduced by individual citizens on social media. However, significant numbers of reproductions on non-social media web sites were discovered under a specific set of circumstances. I highlight these trends that popular handouts follow and use them to construct a "shareability formula" that I suggest maximizes the spread of handouts online if followed. I then discuss the implications of these trends for public discourse on Canadian digital media and the changing dynamics between the PMO, mainstream news media, and a more responsive public.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".