Bird's Eye View: The Construction of Identity and Community on Social Media among Cirque du Soleil Performers
Bibliographic record
Abstract
Although the circus has been around for hundreds of years, it is still a large part of the entertainment industry that draws substantial crowds and interest. Cirque du Soleil— a Québécois contemporary circus— attracts a lot of attention because of its aerial and acrobatic performances. Fans can experience the circus in person at a performance, but they can also interact online by watching performers on social media. TikTok— a social media platform where creators share videos up to three minutes in length— is a great place for Cirque du Soleil performers to gain a new audience and interact with their fans. In this research, I sought to answer the following questions: How are Cirque du Soleil performers negotiating their self-identity on TikTok? What are the shared values of the Cirque du Soleil community on TikTok? How do the interactions between Cirque du Soleil performers and their followers further shape their community? Previous research focuses on why individuals join Cirque du Soleil but has done little to evaluate how performers construct a shared community ethos while expressing themselves individually. This research will do both within the context of interactions with their large fan base on social media.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".