Mythology as a Conceptual Bridge for Teaching Science
Bibliographic record
Abstract
Service courses provide unique teaching opportunities in bridging academic disciplines both within and beyond the traditional boundaries of science. Drawing primarily (but not exclusively) from examples in the geosciences, the first year undergraduate level course Earth, Art and Culture, offered by the Department of Earth Sciences at Western, aims to impress upon students relationships between science and culture.\nOne of the most popular topics covered in the course concerns the origin of ancient myths, namely those featuring fantastical beasts such as dragons, the griffin and the cyclops. Traditionally, such beasts have been dismissed as products of overactive imaginations. However, as recently noted by authors such as Adrienne Mayor (2000), there exists compelling physical evidence that these enduring cultural icons manifest early attempts of humans to make sense of the fossil remains of ancient beasts.\nThis presentation aims to show how the comparison and contrast of ancient (mythologic) and modern (scientific) interpretations of natural curiosities (such as fossils) can emphasize to students that our current understanding of the natural world embodies:\n1) The inherent tendency of humans to seek rational explanations for perplexing observations (as reflected in both pre-scientific and scientific interpretations of natural features).\n2) The development of the scientific method as an objective approach to formulating explanations for observations (thus supplanting the supernatural elements of ancient accounts).\n3) The accumulation of knowledge amassed since ancient times through the addition of new observations and the further testing of hypotheses.\nAlthough centred on the geological and biological sciences in this presentation, this basic approach can clearly be modified to suit other scientific disciplines.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.042 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".