Crowdfunding Canadian Theatre: An Exploratory Analysis of Kickstarter Data with US Comparison
Bibliographic record
Abstract
This study presents an exploratory and descriptive analysis of Canadian theatre projects on the Kickstarter platform, using both quantitative and qualitative data.We argue that crowdfunding is particularly relevant to arts entrepreneurship due to its emphasis on engaging an "audience" from the outset.Our quantitative analysis finds that many theatre projects in Canada are successfully raising funds on Kickstarter, with the success rate being similar to that of theatre projects in the US, but on a relatively smaller scale in terms of the amount of money raised.Only the provinces of Ontario, Quebec, British Columbia, and Alberta have more than three theatre projects on Kickstarter within the timeframe of our data, and in total each province is able to raise about ten to forty-thousand dollars per year for theatre projects through this platform.In our qualitative analysis we find that theatre crowdfunding in Canada is geared toward small-scale and subsistence-level funding, which could be construed as somewhat in line with the traditional hesitation to fully embrace profit-making in arts entrepreneurship, but may also be due to the local nature and limited ticketing capacity of theatre projects and what they perceive to be feasibly achievable goals on Kickstarter.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.029 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".