Catch, Engage, Retain: Audience-Oriented Journalistic Role Performance in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it