A “Scientist” on the Radio: Understanding the Framing of STEM to the Public
Bibliographic record
Abstract
News media presentations of STEM (and particularly science) in various formats have been critiqued for the many ways by which they misrepresent both the facts of the discipline and the practices of the discipline and the researchers in them. Another issue is that the material is presented in a format – basically a one-way transmission – with usually little opportunity for questions by the recipients (i.e., readers, listeners, viewers, etc.) to be addressed when they don’t understand something. One news media format which might allow this dialogic activity is the radio call-in show format which is structured so that the public can ask questions of a “scientist” with the opportunity for follow-up questions to address what are discontinuities in the listener’s understanding. In this paper we document the processes by which listener interests ultimately end up discussed in the radio broadcast and what influences the “science” that is presented on-air. Our analysis reports the ways in which the STEM topics and content are mediated by radio station personnel, often times distorting the factual content available to the public and misrepresenting the practices of the research fields, as they engage in information management practices which are typical of opinion-driven shows (such as those on the topics of politics or sports) which are designed to create controversy and drama to increase ratings.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".