Bibliographic record
Abstract
Since its inception, science news journalism has been accompanied by the perception that journalists need help in their interactions with scientists. Over the past 20 years, Science Media Centres (SMCs) have been established in the United Kingdom, Canada, Australia, New Zealand and Germany for this purpose. These intermediary organisations provide journalists with free-to-use content in the form of press briefings, summaries, scientists&s; expert statements and background information on controversial science topics and new scientific research. SMCs have been both celebrated and criticised for their potential, on the one hand, to facilitate journalists&s; access to scientific expertise and, on the other, to set the journalistic agenda on scientific topics of public interest. A particular concern is the extent to which SMCs perform ‘science PR’ for academic institutions, researchers and publishers. This chapter considers SMC Germany, which, unlike other SMCs, was not founded by academic or political elites, but by science journalists. This identification with journalism shapes the organisations&s; editorial practices, professional criteria and self-understanding, but also the pressures under which its journalists work. The chapter discusses the extent to which SMC Germany manages to supplement science journalism with its content, rather than reinforce the strategic communication efforts of scientific actors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.031 |
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".