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Record W2999971706 · doi:10.51355/jstem.2015.16

A “Scientist” on the Radio: Understanding the Framing of STEM to the Public

2015· article· en· W2999971706 on OpenAlexaff
G. Michael Bowen, Richard Zurawski, Anthony Bartley

Bibliographic record

VenueJournal of Research in STEM Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRadio, Podcasts, and Digital Media
Canadian institutionsLakehead UniversityMount Saint Vincent University
Fundersnot available
KeywordsFraming (construction)Public relationsDialogicSociologyPolitical scienceMedia studiesPedagogyEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.477
GPT teacher head0.483
Teacher spread0.006 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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