Bibliographic record
Abstract
The purpose of the study is to explore if and how high-budget and large-scale stage designs can translate to smaller stages with lower budgets. By examining the works of successful opera designers Es Devlin, Vicky Mortimer and Nicky Gillibrand, clear patterns emerged from each: Es Devlin often has an over-exaggerated use of perspective in her designs; Vicky Mortimer plays with contrast in scale, colour and texture; Nicky Gillibrand explores colour palettes and textures. Each of these specific design elements were translated from their original large-scale setting into a small 15 by 15 foot scale model. After asking audience members to engage with the models, their responses suggested that playing with perspective is a very successful method to make small designs feel grand. These findings can be applied to theatre, opera and performing art companies who are looking to increase the perceived production value of their performances without increasing budgets. Further, by applying the traditional scientific method to practice-based research in the arts, this project demonstrates that theatrical ideas can be made easily accessible to wider communities, and that such methods can contribute to potentially inventive interdisciplinary 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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".