Science Storytelling beyond the Dramatic Arc: Narrativity and Little Red Schoolhouse Principles in Science Communication
Bibliographic record
Abstract
Narrative is widely recommended for improving science communication, yet the main approach to science storytelling is limited and limiting, advocating fixed dramatic arcs and the ideal of narrativehood, the absolute quality of being a coherent narrative. Neglected by this approach, I argue, are the finer grained linguistic patterns that give texts local narrativity, the quality of being narrative in a scalar, adjectival sense. I harmonize narrativity with the well-established principles of clear technical writing developed by Joseph Williams, then demonstrate how these principles might be used and taught through a comparative reading of several texts discussing a single topic in genres ranging from amateur blogs to specialized scientific journals. The narrativity-based approach has several advantages. It avoids the reductionism of the template-based approach, as well as its questionable dependence on narrative structures derived from the arts and entertainment. In terms of adoption by scientists and other science communicators, the approach also has the advantage of not requiring a radical overhaul of current communicative practices; it also reduces the difference between technical and public-facing writing. In short, the approach proposed here offers a workable and effective way to telling science stories with minimal simplification or distortion of scientific information.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".