Participative Gatekeeping: The Intersection of News, Audience Data, Newsworkers, and Economics
Bibliographic record
Abstract
The use of metrics and analytics is becoming pervasive in newsrooms the world over. However, there is a lack of consistent terminology in scholarly literature with regards to such practice and a dearth of studies that compare practice on a wider scale that prevents more holistic understanding of how audience data are being used on the newsroom floor. These issues are addressed with the development of a new participative gatekeeping model with three previously unidentified channels of gatekeeping specifically related to the use of audience data: promotional, for the type of short-term gatekeeping done on news site homepages that involves tracking real-time metrics to position content, often tied to traffic targets; developmental, or longer-term strategies that shape future coverage and are grounded in hypotheses of audience behaviour gleaned from analytics; and a third more porous channel of experimentation where such hypotheses are tested. These channels were observed in ethnographic research in six newsrooms in three different countries at media outlets with diverse sources of revenue: Norway’s public broadcaster, NRK; Canada’s subscriber-based national news agency, The Canadian Press (CP); and two local newsrooms working within larger media organizations, The Hamilton Spectator in Canada, and The Bournemouth Daily Echo in England. Through a sociological lens, this article distinguishes language to best document the complex processes interconnected with audience data, identifies similarities in practice that override media or revenue systems, and explores how the audience, through the participatory mechanisms of metrics and analytics, shapes newsroom practice.
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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 | 0.058 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.031 | 0.030 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".