An investigation of attendee motivations at theatre festivals: a case study of Manipulate Theatre Festival
Bibliographic record
Abstract
The aim of the study is to find out why people are motivated to attend a specific theatre festival. A gap is identified in the existing literature of studies on theatre festival motivations and this study intends to address this gap. The study took the form of a case study and investigated specifically the attendees of the Manipulate Theatre Festival. The research was conducted using two research methods, ethnography and a survey. These methods were chosen so that the limitations of one could be compensated by the advantages of the other. In addition the ethnographic technique enabled participants to reflect their attitudes and opinions more clearly to the researcher. The survey was collected over a period of three days whereas ethnography was used in the form of ‘casual chat’ when collecting survey responses and at two of the shows, ‘Appel d’Air’ and ‘Luvos’.\nThe main finding of the study was that people are most motivated to attend the Manipulate Theatre Festival due to ‘Cultural Exploration’ and least motivated by the factor of 'Family Togetherness'. An extra motivating factor of 'Experience' was incidentally discovered through the use of the Ethnographic technique. The study also revealed that the demographic variables of age, gender, visitor type and residential status affected the strength of motivations ascribed by the attendees.\nThe results of the study showed similarities with previous research on what motivates people to go to festivals. The attendance motivations of the Manipulate Theatre Festival has most in common with the Winnipeg Theatre Festival which suggest that there may be a universal scale of theatre festival motivations. To validate this suggestion, further research is needed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".