Revenue Development for Applied Theatre and its Perceived Impact on Organizational Social Goals in Ontario, Canada
Bibliographic record
Abstract
This study seeks to examine the question: what do managers of applied theatre organizations in Ontario, Canada perceive as the implications of revenue development on the social potentialities of their organizations? Resource dependence theory is a broad field of study, with conflicting branches of thought. Some suggest that not-forprofits should increase fundraising activity, while others suggest that not-for-profits should remain wary of the deradicalizing influence of funders. The space of potentiality created by applied theatre offers a unique opportunity to examine this tension as practitioners are aware of how process, spaces of potentiality, and social goals are intertwined. To examine the research question, this study adopts a case study methodology. This study consists of nine participants responsible for revenue development in an applied theatre organization. All participants are geographically located in Ontario, Canada, and the study took place during the COVID-19 pandemic. This study uses semi-structured, one-on-one interviews and a focus group to gather data. The interviews were turned into a transcript, which was coded using a data-driven codebook. All interviews took place over Zoom to prevent possible harm from COVID-19. The research makes several key recommendations based on the discussion. They are based on two primary conclusions. The first being that funders in Ontario do alter how applied theatre organizations approach their social aims. The second being that, with several changes, revenue development could assist applied theatre organizations in furthering their own social aims.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".