To be genuine in artificial circumstances: evaluating the theatre analogy for understanding teachers’ workplace and work
Bibliographic record
Abstract
This paper provides teachers and teacher educators with food for thought by developing a broad, contemporary re-evaluation of the often-used analogy between teaching and the theatre. It does so by synthesizing insights from scholarly works in education with insights from writing about theatre, including both historical work and published interviews with practicing stage actors. This approach throws into relief particular ways in which teaching does and does not resemble acting as described by its present-day practitioners. A key parallel is observed between the central challenges faced by teachers and actors: acting requires being truthful in imaginary circumstances, while teaching requires being genuine in artificial circumstances. Using work on bildung, the nature of this challenge is examined, and a call is made to help teachers and students better appreciate the intimate, reciprocal and shared nature of good teaching – a challenge in a culture where corporate interests aggressively promote personalized and “anytime, anywhere” learning. The paper also addresses the phenomenon of massive online courses, which enthusiasts like to believe teach themselves. This idea, I suggest, is as absurd as the notion that a great theatre building could obviate the need for a strong cast. .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.012 |
| 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".