Segmenting the audience attending a military music festival
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose and introduce a new classification model to segment a nation’s cultural tourists based on their motivations to travel to a military music festival. Little research is apparent about the types of people, and their motivations, who attend these types of festivals. In addition, the research investigates the impact of military music festivals on the concepts of patriotism and national identity. Design/methodology/approach The research approach involves empirical testing of a Canadian audience attending the Royal Nova Scotia International Tattoo, a longstanding annual musical event held in Nova Scotia, Canada, that pays tribute to the country’s military heritage. A proposed classification model that includes two dimensions is applied, which investigates: motivation to attend the event and kinship to Canada’s military and naval traditions. Findings Findings provide a better understanding of the diversity of the Canadian cultural tourist audience attending a military music display in terms of tourists’ demographics, experience of the show and the desire to return. This research also provides new insights as to the ability of a military musical event to arouse emotions of national pride, patriotism and strengthen national identity. Originality/value This research is important to event sponsors and organizers of military music events as they attempt to maintain productivity and attendance growth in an increasingly competitive entertainment environment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".