Bibliographic record
Abstract
In this editorial, the editor reflects on the current political climate in the US and its impact on theatre education. The editor then introduces this issue, in which our contributors document and reflect on innovative educational theatre practices for youth theatre and theatre for young audiences, in higher education, and research methodologies. Sharon Counts advocates for civic and community engagement programs as one prominent and effective method to foster synergy between communities and arts organizations. Maddie N. Zdeblick and Noëlle GM Gibbs investigate their dynamic use of creative drama to explore social justice in youth theatre. James Woodhams analyzes Back Alley Puppetry Parade performances during the COVID-19 pandemic to document pivot-spaces and kinesthetic spectatorship. David Overton shares a heuristic and phenomenological self-study about the Long Island Classics Stage Company. On the higher education front, Ellen Redling proposes methods for enhancing critical thinking skills and ethical responsibility as revealed through two case studies. Dermot Daly uses a similar methodical approach, examining social justice and fringe theatre in higher education. Finally, Nicholas Waxman deconstructs the rise of theatrical inquiry as a research methodology in arts education, and Brenda Burton presents a literature review on the application of teaching and deep learning strategies for the drama-based instruction (DBI) practitioner and researcher.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.041 | 0.035 |
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".