Individual truth judgments or purposeful, collective sensemaking? Rethinking science education’s response to the post-truth era
Bibliographic record
Abstract
Science education is likely to respond to the post-truth era by focusing on how science education can help individuals use scientists’ epistemological tools to tell what is true. This strategy, by itself, is inadequate for three reasons. First, science does not actually offer foundational truth, and incautious assertions about scientific truth can make the problems of the post-truth era worse. Second, scientific knowledge offers only part of the solution to personal and policy problems and must be reconstructed in context. Third, people think about and act on science in social context—both as members of their social and cultural groups and with other members of those groups. Taken together, these arguments suggest that we should be focusing on a different question: How can science education help people work together to make appropriate use of science in social context?
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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 | 0.055 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.065 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".