Why I teach the controversy: using creationism to teach critical thinking
Bibliographic record
Abstract
Creationism and intelligent design are terms used to describe supernatural explanations for the origin of life, and the diversity of species on this planet. Many scientists have argued that the science classroom is no place for discussion of creationism. When I began teaching I did not teach creationism, as I focused instead on my areas of expertise. Over time it became clear that students had questions about creationism, and did not understand the difference between a scientific approach to knowledge and non-scientific approaches. This led me to wonder whether ignoring supernatural views allowed them to remain as viable "alternatives" to scientific hypotheses, in the minds of students. Also, a psychology class is an ideal place to discuss not only the scientific method but also the cognitive errors associated with non-science views. I began to explain creationism in my classes, and to model the scientific thought process that leads to a rejection of creationism. My approach is consistent with research that demonstrates that teaching content alone is insufficient for students to develop critical thinking and my admittedly anecdotal experience leads me to conclude that "teaching the controversy" has benefits for science students.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".