Metrics and analytics in the newsroom: an ethnographic study exploring how audience data are changing journalistic practice
Bibliographic record
Abstract
<p>The use of metrics and analytics is now embedded in and directly impacting newsroom practice and routines. As audience data are used to shape and promote content that help form societal narratives, development of best practice is crucial, not only to enhance fulsome public discourse but as a means of reputation building for media outlets fighting to retain relevance and public trust, both of which are intrinsically tied to revenue and/or funding. This thesis explores the potential conflicts between journalism’s mandate to keep the public informed through quality, contextualised news coverage and the use of metrics and analytics to build scale and a sustainable business model. Empirical research is based on ethnographic observation in four news organisations on two continents in three different countries: Norway’s national broadcaster, NRK, which has developed its own analytics system that uses both qualitative and quantitative data; The Canadian Press, Canada’s national news agency, which is exploring ways to track how its content is being used with little direct access to audience data; The Hamilton Spectator, a local newspaper in Canada making the shift from print to digital; and a similarly sized and situated local paper in the United Kingdom, The Bournemouth Daily Echo. Participant observation and interviews were used to investigate how metrics and analytics impact newsroom routines; how journalists feel their work is impacted by the use of audience data; and how practices pertaining to the use of metrics and analytics are challenging the boundaries of journalism. The thesis employs a bricolage of theories within a sociological framework, through the lens of media logic, and draws on the author’s own perspective of working in a newsroom and, currently, in an academic media faculty. The research provides observed examples of the ways in which changing boundaries are impacting definitions of journalism and who is a journalist; it proposes best practice for the use of metrics and analytics in newsrooms that might better situate media outlets to serve their communities and survive in a rapidly changing media landscape; it offers suggestions for media scholars on best practice to perform research that better reflects newsroom routines particular to the use of metrics and analytics; the thesis contributes a new gatekeeping model that identifies two primary channels related specifically to the use of metrics and analytics: promotional and developmental; finally, the thesis demonstrates how a bricolage of complementary theories and the selection of multiple sites of study might best support the reflexive investigation of complex social structures within a rapidly changing field.<br> </p>
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How this classification was reachedexpand
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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".