Holding science to account: A qualitative study of practices and challenges of watchdog science journalism
Bibliographic record
Abstract
Amid growing concerns about fraud, misconduct, and related issues, journalists can play an important role in engaging the public with problematic science. Integrating the Hierarchy of Influences model (Shoemaker & Reese, 1996) and Stages of News Production framework (Domingo et al., 2008), this study examines how such “watchdog” science journalism is practised, facilitated, and challenged in the contemporary science and media landscape. Through framework thematic analysis of 21 semi-structured interviews with journalists who have reported on research integrity issues for Canadian and UK media outlets, it illuminates the time- and labour-intensive nature of this form of journalism, which often requires multiple interviews and extensive document research to reach a sufficient threshold of evidence. As a result, the feasibility of potential stories sometimes plays a bigger role in whether they are reported than their public importance, especially in resource-poor newsrooms. Added to this are challenges related to uncompliant sources, unavailable evidence, legal risks, and story saleability in a metrics-driven media landscape. Collectively, the findings underscore the precarity of a form of journalism which has arguably never been more important, but also never more under threat.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".