From spotlight to fluorescent bulb: aesthetic dimensions of personal, practical knowledge in an actor training to be a high school teacher
Bibliographic record
Abstract
This paper can be conceived as one story embedded in a second story, in which the ‘outer’ narrative, involving the theoretical and methodological framework, is that of my search for a means of defining, articulating and implementing an aesthetic epistemology in both academic research and in teacher education/development (a search that is advanced but not resolved in this article). In the ‘inner’ story, I present the report of a qualitative research study with a participant in transition from a career as a theatre professional to one as a high school drama teacher. The inquiry focuses on the aesthetic dimension of personal, practical knowledge, an aspect of personal, practical knowledge previously unacknowledged. This study focuses on one research participant, who in the midst of a successful career as an actor in mega-musicals decided to become a high school teacher. ‘Ella’ has performed in Miss Saigon, Beauty and the Beast, and Showboat in Toronto and around North America, and her story foregrounds the pedagogical role of storytelling, being ‘on’, nuance, discovering the familiar in the new, and drama as a methodology.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".