Inspired by the Cosmos: Strategies for Public Engagement in Nonpolicy Contexts
Bibliographic record
Abstract
Public engagement scholarship has explored science-policy settings at length. This work is being complemented by growing scholarly attention to engagement outside of policy spaces. As this expanding focus indicates, these spaces, where publics engage science with lower stakes and less confrontation, should be taken seriously. We explore what engagement in such contexts can look like, offering insights from one site, the planetarium. When coupled with a commitment to fostering public-science conversations, engagement strategies like dialogue, storytelling, analogy, and fostering agency can be instrumental for publics to be heard, which can enrich the efforts of nonpolicy venues. In turn, studying nonpolicy contexts can broaden our understanding of engagement.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.044 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 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".