Bibliographic record
Abstract
Analytics are now embedded in newsroom practice. In a form of participative gatekeep-ing, the ability to track how the audience absorbs information is shaping editorial content.Although there is much discussion that engagement metrics, like time spent, are moreimportant than pageviews, many advertisers are still more interested in clicks than count-ing time, some newsrooms still have pageview targets, and the pageviews metric is oftenused as a simplistic measure of reach. As such, digital editors sit cemented to monitors,working to decipher what stories have or are gaining traction. Using this information,they choose placement of content, enhance stories, and share stories via social media tobuild traffic, then repeat this frenetic cycle in a seemingly endless loop. But at what cost?How does the focus on metrics affect best practice in the newsroom and, potentially,information sharing in the public sphere? This article examines the impact of audiencedata on practice atThe Hamilton Spectator, a local newsroom in Canada, to explorewhether traffic-based metrics and the use of analytics impede the ability to meet jour-nalistic standards, and/or build bigger, more informed and engaged audiences
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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 | 0.054 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.038 | 0.052 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".