Science, technology, and society : a sourcebook on research and practice
Bibliographic record
Abstract
Introduction D. Devraj Kumar, D.E. Chubin. 1. Real Science Education: Replacing 'PCB' with S(cience) through-STS throughout all levels of K-12: 'Materials' as one approach R. Roy. 2. The Development of Civic Scientific Literacy in the United States J.D. Miller. 3. STS Science in Canada: From Policy to Student Evaluation G.S. Aikenhead. 4. Trade-Offs, Risks, and Regulations in Science and Technology: Implications for STS Education J.C. DeFalco. 5. Thoughts About the Evaluation of STS: More Questions than Answers J.W. Altschuld, D. Devraj Kumar. 6. Science, Technology, Society, and the Environment: Scientific Literacy for the Future K.B. deBettencourt. 7. Marginalization of Technology within STS Education in K-12 Schools in America D.W. Cheek. 8. Student Understanding of Global Warming: Implications for STS Education Beyond 2000 J.A. Rye, P.A. Rubba. 9. STS Education for knowledge professionals J.S. Hauger. 10. Reculturing Science: Politics, Policy, and Promises to Keep D.E. Chubin. 11. Trends and Opportunities in Science and Technology Studies: A View from the National Science Foundation E.J. Hacket.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.089 |
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".