Political Photography, Journalism, and Framing in the Digital Age: The Management of Visual Media by the Prime Minister of Canada
Bibliographic record
Abstract
In the digital age, journalists are becoming more susceptible to the packaged visuals of politicians that image handlers are pushing electronically in an attempt to circumvent and influence the mainstream media. These managed photos and videos communicate officialdom, voyeurism, and pseudo-events, ranging from routine government business to a personal side of political leaders. They are designed to frame the subject in a positive light and to promote a strategic image. This article submits that demand for digital handouts of visuals, or “image bytes,” is stimulated by economics and institutional accommodation, including the constant need for Web content and journalists’ eroding access to government officials. A profile of the image management of Prime Minister of Canada Stephen Harper illustrates the jockeying between politicians, PR staff, and journalists over news selection, pseudo-events, framing and gatekeeping. Insights from 32 interviews with Canadian journalists and Conservative party insiders suggests that a two-tier media system is emerging between the small news operations that welcome digital handouts and the mainstream journalists who are opposed. Theoretical themes for international research include examining the implications of political image bytes such as the possible priming effect on journalists who are exposed to constant visual e-communication pushed by political offices.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".