The ‘Great Divide’: How the Arts Contribute to Science and Science Education
Bibliographic record
Abstract
In recent years, there has been a rapid growth in interest about the relationship between the arts and the sciences. This article explores this developing relationship and the suggestion that science and science learning are not complete without the arts. We see three levels at which the arts might improve the teaching and learning of science. The first is at a macro-level, concerned with ways in which subjects (including the arts and sciences) are structured and options for studying them provided and packaged. The second is at the meso-level, guiding approaches constructing science curricula that engage learners through using STS (Science, Technology and Society) contexts. The third is at the micro-level, of pedagogical practices in science and teaching that can be drawn from the arts. The drivers of STEAM (Science, Technology, Arts, Engineering and Mathematics) add new dimensions to the nature of science in the twenty-first century and make science likely to diverge even more rapidly from school science unless new pedagogies, including those from the arts, help close the gap. The result could be a more authentic and engaging school science, one more relevant to the needs of the twenty-first century.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.060 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".