The Power of Stories: Narratives and Information Framing Effects in Science Communication
Bibliographic record
Abstract
This article explores information framing effects by comparing the effectiveness of using logical‐scientific versus narrative information to communicate with consumers about a new biotechnology application (gene editing). Using data from an online survey of 804 Canadian adults, a discrete choice experiment elicits preferences for diverse novel food attributes and technologies, with respondents randomly assigned to different information conditions. We construct a logical‐scientific information condition, written in a scientific style using the passive voice with generalized and impersonal language and attributed to either a government agency or a scientific organization. In contrast, we frame the narrative‐style information condition as a story, using a lively and vivid personal style, and attributed to either a science journalist or a consumer blogger. Data are analyzed using multinomial logit and random parameters logit models. We find that the information format (logical‐scientific vs. narrative) matters: narratives help reduce negative perceptions regarding agricultural and food technologies. We also examine factors that predispose consumers to seek logical‐scientific versus narrative information sources.
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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 | 0.021 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".