Thinking Through Improvisation: Do General Improvisation Studies Belong in a Liberal Arts Curriculum?
Bibliographic record
Abstract
Thinking Through Improvisation implies two meanings: 1) carefully examining all that improvisation encompasses including how it is practiced, and 2) using improvisation to generate ideas or performances. Using a First Year Seminar course I taught for 20 years, I illustrate how a general course in improvisation can introduce students to improvisation as a way of thinking in diverse fields and can strengthen liberal arts skills in critical and creative thinking. Interdisciplinary and multicultural approaches are readily incorporated as are a range of activities including writing, critical reading, performance, and creative problem solving. Risk taking, trust, creativity, adaptability, teamwork, respect for knowledge, abstract and practical thinking and the joy of creative discovery are explored through discussion and practice of improvisation. Scientific explanations of improvisation are compared to subjective experiences of improvisational performance. These activities lay a groundwork for creative explorations of the discipline-oriented curriculum in the range of fields subsequently encountered by liberal arts students.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".