An Educational Bridge across the Cultural Divide: Teaching Art to Science Students and Science to Art Students
Bibliographic record
Abstract
two cultures divide between art and science remains a problem, and it is aggravated by an educational system that compartmentalizes programs of study. Thus a course that focuses on the connection between art and science is a useful corrective. I have had extensive personal experience teaching such an unusual course, The Psychology of Art and Creativity, which is offered as a valid elective for both science and art majors. My experience is that both groups gain a much greater understanding and appreciation of each other's chosen field of endeavour. It is important in such a course to include exposure to actual artworks in every genre, rather than primarily focusing on criticism and analysis. Personal subjective opinions have to be more openly welcomed than in the traditional academic approach, while making a clear distinction between personal preference and objective judgment. One useful pedagogical tool is an informal reflective journal where students write as much, or as little, as they want about each of the class themes. Also useful are online discussion forums where students can bounce ideas off of each other and share their enthusiasms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".