Catch, Engage, Retain: Audience-Oriented Journalistic Role Performance in Canada
Bibliographic record
Abstract
To understand audience-oriented journalistic role performance, one must understand how journalists conceptualize and cater to their audience. Giving the audience what it wants is a complex endeavor, with varying goals and hybridized end results, in newsrooms with fewer resources serving increasingly polarized audiences. Through a triangulation of data—content analysis at the subdimension level to examine the range and hybridity of audience-oriented journalistic product presenting the civic, service and infotainment roles; a survey to identify journalists’ attitudes toward the use of audience data and social media in their work; and interviews with journalists that revealed how their journalistic practice and audience perceptions were impacted by quantitative (metrics and analytics) and qualitative data (comments/social media interactions)—this research fills a gap in understanding about the connection between journalists, their audiences, and audience data when it comes to journalistic role performance. Findings show that in Canada the infotainment role is a significant part of reporting, but entertaining often comes with a goal of educating, as does service journalism. There are no “bad” journalistic roles, but there are a lot of journalists trying to figure out which ones might best catch, engage, and retain an ever-shrinking news audience.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".