Explaining Nutrition to the Wider Public: An Analysis of Spoken Science Popularization Texts
Bibliographic record
Abstract
In private or public communication, the successful transmission of information depends on a variety of decisions involving pragma-discursive strategies such as simplification, transparency and clarity, which are meant to make the message understandable to the wider public. Science popularization is paramount to provide non-experts with scientific evidence to help them make informed decisions and take action in multiple aspects of their lives, including their health and wellbeing. When communicating to a lay audience, recontextualization strategies (e.g., Calsamiglia & van Dijk, 2004; Gotti, 2014) are widely used to illustrate complex concepts and define science jargon that may otherwise be unfamiliar. Adopting a corpus-assisted approach, this study investigates explanatory strategies in a corpus of 24 spoken science popularization texts on nutrition. Specifically, the corpus consists of broadcast interviews, TED Talks and YouTube videos/podcasts where professionals discuss nutrition facts to educate their audience on how their diet choices can hinder or promote wellbeing. The first step of the study entails the extraction of scientific jargon with corpus analysis software SketchEngine (Kilgarriff et al., 2014); the second step consists in the manual filtering of concordance lines of the selected terms; finally, identified explicatory strategies will be categorized and contextualized within popularization discourse.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".