Locating Scientific Citizenship: The Institutional Contexts and Cultures of Public Engagement
Bibliographic record
Abstract
In this article, we explore the institutional negotiation of public engagement in matters of science and technology. We take the example of the Science in Society dialogue program initiated by the UK’s Royal Society, but set this case within the wider experience of the public engagement activities of a range of charities, corporations, governmental departments, and scientific institutions. The novelty of the analysis lies in the linking of an account of the dialogue event and its outcomes to the values, practices, and imperatives—the institutional rationality—of the commissioning organization. We argue that the often tacit institutional construction of scientific citizenship is a critical, and relatively undeveloped, element of analysis—one that offers considerable insight into the practice and democratic implications of engaging publics in science and science policy. We also present evidence indicating that over time the expanding ‘‘capacities’’ associated with dialogue can act in subtle ways to enroll other elements of institutional architectures into more reflexive modes of thinking and acting. In the concluding section of the article, we consider the ways in which research and practice could (and we believe should) engage more squarely with facets of institutional context and culture.
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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 | 0.046 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.017 | 0.103 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".