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Record W3031467418 · doi:10.34053/artivate.9.1.90

Crowdfunding Canadian Theatre

2020· article· en· W3031467418 on OpenAlexaffabout
Mohammad Keyhani, Safaneh Mohaghegh Neyshabouri, Abbas Hosseini Amereii

Bibliographic record

VenueArtivate A Journal of Entrepreneurship in the Arts · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExploratory analysisDescriptive statisticsScale (ratio)Quantitative analysis (chemistry)Exploratory researchGeographyQualitative analysisAdvertisingQualitative researchComputer scienceBusinessSociologyCartographyData scienceStatisticsSocial scienceMathematics

Abstract

fetched live from OpenAlex

This study presents a comprehensive exploratory and descriptive analysis of Canadian theatre projects on the Kickstarter platform, using both quantitative and qualitative data. In the country-level analysis, comparative statistics are provided with Kickstarter projects based in the United States. 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 Ontario, Quebec, British Columbia and Alberta, have more than three theatre projects on Kickstarter and of these, only Quebec has a proportion of Canadian theatre projects (and successful projects) on Kickstarter that are lower than its proportion of Canada’s population. In our qualitative analysis we find that theatre crowdfunding in Canada has a strong philanthropic component, and highlight limitations with Kickstarter data and the challenges of measuring success in crowdfunding more generally.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.226
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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