What can we learn from 25 years of PUS survey research? Liberating and expanding the agenda
Bibliographic record
Abstract
This paper reviews key issues of public understanding of science (PUS) research over the last quarter of a century. We show how the discussion has moved in relation to large-scale surveys of public perceptions by tracing developments through three paradigms: science literacy, public understanding of science and science and society. Naming matters here like elsewhere as a marker of "tribal identity." Each paradigm frames the problem differently, poses characteristic questions, offers preferred solutions, and displays a rhetoric of "progress" over the previous one. We argue that the polemic over the "deficit concept" voiced a valid critique of a common sense concept among experts, but confused the issue with methodological protocol. PUS research has been hampered by this "essentialist" association between the survey research protocol and the public deficit model. We argue that this fallacious link should be severed to liberate and to expand the research agenda in four directions: contextualizing survey research, searching for cultural indicators, integrating datasets and doing longitudinal analysis, and including other data streams. Under different presumptions, assumed and granted, we anticipate a fertile period for survey research on public understanding of science.
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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 | 0.265 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.025 | 0.063 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".