Exploring the Role of Disruptive Periods in the Digital Transformation of Theatre and Performing Arts
Bibliographic record
Abstract
This article investigates the impacts of the disruptive periods of uncertainty and great change caused by processes such as the 2020 global Covid-19 pandemic, on the performing arts industry, with a specific focus on theatre. We use a combination of systematic literature review and semistructured interviews with professionals in the field to explore how the shift to digital platforms has reshaped production, distribution, and consumption within this traditionally live interaction-dependent sector. This article identifies key transformations in the sociotechnical regime of the performing arts, highlighting the accelerated integration of digital technologies and the consequent shifts in audience engagement and economic models. Theoretical contributions of this article include insights into the resilience and adaptability of creative industries in the face of regulatory and environmental changes, while practical implications point to the need for innovative economic strategies and policy support to bridge the digital divide.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".