Drama as a Digital Learning Space in the Political Science Classroom
Bibliographic record
Abstract
The richness and complexity of the study of conflict can easily overwhelm students and impact their retention of knowledge, especially during the pandemic and its challenging instructional modes. Yet, remote communication tools can offer advantages in promoting learning, enhancing pedagogy and fostering student community. Employing innovative peer-to-peer, experiential techniques, like asking students to use remote learning platforms to read parts of an ancient tragedy in a performative way and connect it to their written assignments, can increase engagement with the material, foster cooperation and mutual learning, and provide a memorable learning experience. This paper illustrates this application to a remotely-taught undergraduate course ‘On War’ at the University of Toronto Mississauga via a collaborative exercise co-designed with Classics and Theater Studies, and an award-winning Scottish director and her associate towards a student-led virtual performative reading of an adaptation of Euripides’ The Women of Troy to the modern conflict in Syria.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".