Raining on the parties’ parade: how media storms disrupt the electoral communicational environment
Bibliographic record
Abstract
Literature on agenda-building dynamics has neglected to assess the impact of contextual factors on the interplays between the issue attention of political actors and of the media. I fill the void by highlighting how media storms cause significant changes to the electoral communicational environment. Using a custom dataset compiled through an automated content analysis, I empirically examine patterns of issue salience during the 2015 Canadian federal election. The results support three main points. First, media storms do emerge during election campaigns. Second, media storms cause two main types of changes in the informational environment that characterize non-storm periods: (1) a reduction in the variety of issues included in the daily campaign coverage, and (2) a higher concentration of media attention on the storm-generating issues. Third, coverage of media storms compels political parties to engage with them, especially if they can exploit these storms with minimal risks. These findings suggest that some electoral contexts may be less conducive to political actors’ influence. They also offer evidence in support of the mediatization theory, according to which media market logic can take precedence over political normative logic in guiding the decisions of political actors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".