Bibliographic record
Abstract
Journalists and/or their editors appear to avoid science topics and journalists have the reputation of being largely unequipped to handle medical science, environmental science, or any science, in ways that do not distort, misrepresent or misunderstand the science and that do not promote a continuing feud with scientists. Nurse & Tooze (2000) contend that the result of this has been a generally low quality of debate about science issues and increased public anxiety. Low levels of understanding are the outcome of the public, the media, politicians and other opinion-formers having little idea of how science is done and how scientific knowledge is advanced, they argue. Although a convenient solution to this problem is seen in the training of specialist science journalists, such an apparent solution is impractical and, indeed, probably unsuitable. A better solution would seem to be to prepare all journalism trainees for the inevitable encounters with science and technology and especially the issues surrounding ever more contentious developments. This paper sets out to address the central question of whether generalist journalists can be better equipped to deal successfully with science writing and reporting and whether this can be achieved in their normal tertiary education in journalism. It does this in two ways: First, it looks at students' survey responses to a spectrum of questions about science, science in the news and their knowledge of basic scientific concepts to explore the potential of journalism students to cover science matters. Then it examines the perceived quality of journalism students' writing in a survey of source scientists. The conclusion drawn from this is that most journalism students are quite capable performing the task well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.199 | 0.143 |
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".