CLOWN SCOUTING AND CASTING AT THE CIRQUE DU SOLEIL: DESIGNING BOUNDARY PRACTICES FOR TALENT DEVELOPMENT AND KNOWLEDGE CREATION
Bibliographic record
Abstract
A significant part of management in creative organisations is the discovery, development, and engagement of the creative talents. These activities require practices at the intersection of talent management, knowledge management and HR management. In this paper, we observed a bootcamp held at Cirque du Soleil in order to experiment with new casting and training practices for a scarce and specific occupational creative community: clowns. Our study shows that this bootcamp provides context at the borders of distinct practices: recruitment, training, and exploration. This intermediary zone allows the emergence of a boundary practice: the co-construction of what actors of the organisation and members of the communities do, make and learn to connect, create and understand new meaning of their shared reality in performance and exploration. This concept contributes to an improved understanding of the management of scarce talents in knowledge-and-creativity intensive fields, as hi-tech industries, software development, engineering, or creative industries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".