Using Dramatic Monologue for Teaching Social Sciences
Bibliographic record
Abstract
During the welcoming session at the start of every academic year, teachers in Vanier College’s Psychology \nDepartment put on a skit to introduce incoming “psychology major” students, in a concise and entertaining \nmanner, to the three different theoretical approaches currently prevailing in the discipline. In the skit, a teacher \nplays the role of a client who consults a psychotherapist (played by another teacher) for help with a marital \nproblem. Seeking a solution to his problem, the “client” appears on stage three different times and receives \ntreatment from three psychotherapists (played by another teacher) of different theoretical orientations: \nB.F. Skinner, Sigmund Freud, and “Dr. Phil”, the famous American talk-show host (who respectively represent \nbehaviorism, psychoanalysis, and cognitive psychology). Generally speaking, this skit is the first real exposure \nto psychological theories for the new cohort of students. Based on the feedback received afterwards, \nit seems to have made a powerful impression on them. Which explains why we keep putting on the same skit \nyear after year! \nOne reason for the impressive success of this simple skit is quite clear: complex ideas can be effectively \nconveyed to even the most uninitiated in a concise and easily understood manner through dramatic techniques, \nbecause drama is engaging, entertaining, and thought-provoking.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.178 | 0.059 |
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".