Infotaining Canadian Politics? Measuring Infotainment in English-Language Newspaper Coverage of the 2019 Canadian Federal Election
Bibliographic record
Abstract
Abstract Several scholars have noted that many types of news coverage (including political news) are increasingly characterized by an “infotainment” style—defined roughly as the communication of politically relevant information using styles and formats more commonly associated with entertainment-oriented programming. Despite this growing trend and the many findings surrounding its impact on politics and political discourse, very little research has been done on the nature and dynamics of infotainment within the Canadian context. In response, this article seeks to measure and evaluate the scope and nature of infotainment in Canadian political news coverage by (1) outlining a comprehensive conceptual definition of (and rigorous method of studying) infotainment and (2) sharing the results of our mixed-methods discourse analysis of infotainment characteristics within 969 hard news articles published in Canadian English-language newspapers that covered the 2019 Canadian federal election. Our findings demonstrate that there was a substantial presence of infotainment characteristics in this coverage. We discuss the detailed nature of these characteristics and the relationship between the presence of infotainment characteristics and those of the quintessentially “Golden Age” reporting style (often viewed as infotainment's polar opposite), while outlining a variety of broader implications and further research questions raised by these findings.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".