Using multiple languages within an improvised fairytale during online arts-based collaborations
Bibliographic record
Abstract
This article presents an illustration from an online creative arts project in which different languages were used by participants of a small group within fairytale-movement-music improvisation. The participants, consisting of creative arts therapists and students from different regions of the world, including Canada, New Zealand, and China, represented various world cultures and spoke different primary languages. A session was selected by the authors as it represented an example of a natural experiment that emerged from the global arts-based response to the COVID-19 pandemic, offering a unique case study of how art expression can contribute to communities during crisis events. The purpose of the article is to provide suggestions to guide future groups in the use of arts-based improvisation that might improve communication among participants who do not share a common primary language but have shared complex emotional experiences. In addition, the article includes a review of related education, dance, and drama projects that involve different languages and cultures as well as a drama therapy project that addresses the improvised dramatic communication of complex emotional experiences. Furthermore, the article offers a detailed review of one session from the project using an arts-based inquiry and suggests ways to apply multilingual imaginative storytelling within the communication of groups in community and educational cross-cultural settings.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".