Walking on a Tightrope? Analysing Emotional Fragilities as a Competitive Advantage in Cirque Du Soleil’s Creation of Value<br>http://dx.doi.org/10.5585/riae.v9i3.1696
Bibliographic record
Abstract
An emotional relationship between business and consumers can create positive aspects. However, complexity of emotional processes can become a challenge concerning the long-term growth of companies pursuing to implement such a strategy. Therefore, this study aspires to evidence possible fragilities of organizational strategies, which create value through emotional aspects. Consequently, we conducted a case study of the Canadian company Cirque du Soleil, with data collection consisting of: an interview with the firm’s head publicist; in addition to interviews undertaken by secondary data; three months spent amongst one of the company’s shows; and by secondary data from both the electronic and press media. Data analysis and interpretation were established by using the content analysis technique. It was acknowledged that despite the rapid growth and success of the company, there are still certain challenges relating to the continued growth associated with the consolidation of emotional relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".