The Curation of Performing Arts : Curatorial Design of Cirque du Soleil
Bibliographic record
Abstract
[[abstract]]在新時代的數位媒體匯流下，表演藝術團體應用現今的網路行銷觀點和做法，將表演內容擷取予以數位化的方式來呈現，是否真正做到以數位方式儲存的結構化資料，有效率的發揮嶄新雲端科技整合觀念及分析方法。有鑑於此，本研究以國際知名表演藝術產業團體，加拿大「太陽劇團（Cirque du Soleil）」，為目標個案進行探討。第一階段資料蒐集將以SWOT分析官方網站之架構與網路數位內容分析；第二階段則依據初步數位內容分析後，擬妥訪談問題並邀請「太陽劇團」負責數位媒體資源之管理人員受訪，另亦邀請曾加入該團體之臺灣籍表演藝術家受訪；第三階段則以質性研究方法透過前二個階段之程序所整理的資料進行內容分析。本文主要探討太陽劇團如何以系統化方式執行表演策展，並分析在數位資源應用面向之數位策展相關內容，期能以「太陽劇團」如此具有國際性文化創意產業價值與經濟產值的表演藝術團體，讓臺灣不論是觀光劇場定目劇的經營或表演藝術團體的表演策展得以仿效學習之。 In the new era of media convergence, the performing groups that apply Internet marketing and other ideas to show the content or to be retrieved in digitalized way, have not really touched an integration of virtual community for the application of new concepts and methods of cloud computing. Therefore, this study focuses on an internationally renowned performing arts industry groups ”Cirque du Soleil” from Canada. As a case study, in the first phase SWOT analysis is carried out through its official websites structures and other Internet contents. In second phase, official invitations are proposed for interviews of ”Cirque du Soleil” who are responsible for the management of digital media resources, and performers from Taiwan who have joined the Cirque du Soleil. In third phase, qualitative research methods are adopted for coding and analysis of the data collected in the first two phases. This paper mainly discusses how ”Cirque du Soleil” operates performing arts curation, and analyzes the digital resource applications for the relevant content. Thus, for such an international performing group of cultural and creative industries, its value and market strategies could serve as a reference for the scenario operation of tourism theaters in Taiwan.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".