The contribution of science‐rich resources to public science interest
Bibliographic record
Abstract
Abstract This preliminary study examined the effect that five major sources of public science education—schools, science centers, broadcast media, print media, and the Internet—had on adults' science interest values and cognitive predispositions . Over 3,000 adults were sampled in three U.S. metropolitan areas: Los Angeles, California, Phoenix, Arizona, and Philadelphia, Pennsylvania. To minimize potential sampling bias, the results were weighted by current U.S. Census data to be comparable to demographics from each of the three jurisdictions. Participants were asked to self‐report their current and early adolescent usage of these five science‐related resources, the quality of their experiences with each, and their current abilities, values, and cognitive predispositions relative to science. Data showed that overall, a broad cross‐section of adults living in these cities engaged in a wide array of science‐related activities and that large majorities did so frequently. Nearly two‐thirds of all respondents self‐reported currently participating in some kind of science‐related activity every week and nearly half doing so daily. Results suggested that having frequent; positive science‐related experiences in‐ and out‐of‐school, both early and later in life, correlated with having a strong interest in and positive perception of science as an adult. Although a diversity of positive science‐related experiences correlated with current adult science interest values and cognitive predispositions, only five factors uniquely and significantly predicted adult science interest, values, and cognitive predispositions in the multivariate models: (a) early adolescent experiences visiting a science center, (b) early adolescent experiences watching science‐related television, (c) adult visits to a science center, (d) adults reading books and magazines about science, and (e) adults using the internet to learn more about science. Discussed are issues of self‐selection, quality of experiences, and the complex and synergistic nature of the science learning ecosystem.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".