Bibliographic record
Abstract
Engaging communication of complex scientific concepts with the general public requires more than simplification. Compelling, relevant, and timely points of linkage between scientific concepts and the experiences and interests of the general public are needed. Pop-culture icons such as superheroes can represent excellent opportunities for exploring scientific concepts in a mental “landscape” that is comfortable and familiar. Using an established icon as a familiar frame of reference, complex scientific concepts can then be discussed in a more accessible manner. In this framework, scientists and the general public use the cultural icon to occupy a commonly known performance characteristic. For example, Batman represents a globally recognized icon who represents the ultimate response to exercise and training. The physiology that underlies Batman’s abilities can then be discussed and explored using real scientific examples that highlight truths and fallacies contained in the presentation of pop-culture icons. Critically, it is not important whether the popular representation of the icon shows correct science because the real science can be revealed in discussing the character through this lens. Scientists and educators can then use these icons as foils for exploring complex ideas in a context that is less threatening and more comfortable for the target audience. A “middle-ground hypothesis” for science communication is proposed in which popculture icons are used to exploring scientific concepts in a bridging mental landscape that is comfortable and familiar. This approach is encouraged for communication with all nonscientists regardless of age.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.431 | 0.168 |
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".