Bibliographic record
Abstract
This paper raises three concerns: 1. Pedagogy. Effective drama demands a constructivist pedagogy (Wagner,1998), one built upon questions, discourse, reflection, and, if it is to be transformative, action (Brooks & Brooks, 1993). Unfortunately, most teacher education takes place within pre-service programmes and schools that practice the traditional educational model (Windschitl, 2002). When many drama/theatre teachers have little experience with a still anomalous pedagogy and can receive little knowledgeable support for their teaching, what in their drama teaching are they valuing and assessing? 2. The art form. We know of the lack of theatre experience that pre-service teachers bring with them (Miller & Saxton, 2000), and this is exacerbated by the limited courses offered in theatre/drama within generalist teacher education programmes. There are theatre requirements for entry into secondary school theatre/dramatic arts pre-service teacher education, but the quality and content varies significantly in depth, extent and practice, depending upon locale and the focus of the degree. Where then is the depth of knowledge and experience to support the application of standards to student work? 3. Standards application. Given the above, how can standards in the art form become internalized and actualized in our classrooms?
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".