Bibliographic record
Abstract
New technologies and audience data are transforming local, legacy newsrooms that are built on tradition but adjusting practice to keep pace with a rapidly changing media landscape, deep revenue cuts, and shifting means of content consumption. Through ethnographic research, this chapter explores how digital production and the burgeoning use of metrics and analytics are changing the fabric of what makes local, local, along with challenging definitions of journalism and the traditional values associated with news. It compares and contrasts practice in two newsrooms: Canada’s The Hamilton Spectator , run under the umbrella of Metroland Media, and the Bournemouth Daily Echo in England, operated within the Newsquest group. Can the use of analytics give fresh insight into the audience and help save local newspapers from their seemingly steady decline? Or is a lack of time and training to use analytics effectively contributing to an erosion of journalistic values? The chapter aims to further understanding of the effects of digital production, how a growing reliance on metrics and analytics is impacting editorial decision-making, and how effective use of audience data might help local newsrooms survive.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".