Welcome to Ducks on the Moon, an Afternoon of Music Theatre as Professional Development about Families and Autism Spectrum Disorder
Bibliographic record
Abstract
As a playwright-actor-researcher, Burke (2010) created, toured, and published a one-woman play, Ducks on the Moon (DOTM) about her (and her family’s) journey from denial to acceptance to celebration of her youngest son’s diagnosis of autism spectrum disorder (ASD) during his first 5 years. DOTM is not only a drama, but also an arts-based educational research (ABER) project; Burke (2010) labelled her methodology “performative memoir.” In this current project, seven songs were added to the play. As authors-researchers we performed DOTM as a workshop to pre-service and in-service teachers. In particular, we wanted to see if DOTM might serve as professional development (PD) or might become part of a teacher education curriculum. The research questions were as follows: What was the audience’s general reaction to the play-with-music DOTM? And, what might be the implications of such an experience for educators? Did the audience feel that the play-with-music was useful as a vehicle to relay information about ASD and family–professional relationships? Might DOTM serve as PD for in-service teachers or as part of curriculum to prepare pre-service teachers for inclusive practice? We uncovered five inter-related themes: the audience enjoyed DOTM and claimed it was also it was educational, they experienced a range of emotions, they stated that parents need to come to terms with their child’s diagnosis in their own time, many teachers identified with Burke as a mother, and finally, DOTM would make productive PD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".