The Value in Science-Art Partnerships for Science Education and Science Communication
Bibliographic record
Abstract
Just a fraction of the scientific knowledge produced in laboratories reaches a lay audience. Most of our communication with the public gets lost in translation because of the difficulties that science communication poses to scientists. Among other obstacles, differential exposure to scientific and critical thinking, discrepancies with social narratives, and communication training based in the deficit model add on top of a practice established on avoiding emotionality. In this context, effective communication requires the use of emotions, which are crucial to establishing trust. This commentary provides a rationale for collaboration with graphic design and fine arts to use emotions in science communication and education. It starts by proposing the two-way engagement model as a replacement for the deficit model. Next, it offers a neuroscientific basis for the use of emotions in establishing trust. Finally, it finishes profiling the Convergence Initiative's efforts to establish bridges across disciplines and communicating science with the public through art.
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 | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".