Bibliographic record
Abstract
While it may seem counterintuitive to promote creativity through “copying,” teaching drama and theatre through adaptation-based methods fosters creative and critical thinking. Learning how many of the great masterpieces of the dramatic canon are actually adaptations themselves pierces the mystique surrounding the creative process, making it less intimidating to students who have been led to believe that great art is created out of thin air by artists possessed of divine genius. Learning the tools and techniques of adaptation puts creativity within the grasp of “ordinary” students and motivates them to create works of their own by emulation, quotation, and parody – which is exactly how many of the great artists learned their trade. Moreover, studying and producing adaptations using the critical techniques of such authors as Anne-Marie MacDonald, Djanet Sears, and Tom Stoppard reveals how critical and creative process are related, not antithetical, and dispels the common fear that thinking critically about creative process somehow diminishes or damages one's creative faculty. This essay draws on current creativity theory and the author's recent dissertation on Canadian adaptive dramaturgy to illustrate some examples of their application to tertiary theatre teaching.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".