Munsch at Play Act Two: Eight More Stage Adaptations by I. Watts & R. Munsch
Bibliographic record
Abstract
Watts, Irene N., and Robert Munsch. Munsch at Play Act Two: Eight More Stage Adaptations. Illus. Michael Marchenko. Toronto: Annick, 2011. Print. Which child should inhabit the role of the protagonist Andrew (“No, no, no, no, no”) in “I Have to Go”? What could be used to make the “sound” in “Jonathan Cleaned Up — Then He Heard a Sound”? How could you dress the titular character of “David’s Father” so that everyone can tell he’s a giant? Munsch at Play Act Two will have your imagination firing with questions such as these. As with the original Munsch at Play, this volume gives readers/thespians eight of Robert Munsch’s tales transformed into stage plays for young performers. Readers familiar with Munsch’s live performances will recognize his distinct rhythmic and participatory elements within Watts’ adaptations. Readers will also enjoy Michael Martchenko’s kinetic illustrations, in his style so well-known from so many Munsch storybooks. Having said this, plays are to be performed on the stage, not just admired on the page. Irene N. Watts’ adaptations succeed for a variety of reasons. The stories have been thoughtfully selected and adapted to provide the variety that makes this sort of book worthwhile. There are stories that can be performed with a finite handful of players, such as “I Have to Go,” and there are stories whose castings can expand as much as you like, including “Pigs,” in which you may include “any number that the pigpen can accommodate.” Watts also includes detailed, approachable notes about staging. She tells readers about suitable spaces for staging each play, such as “works well as a theatre in the round” (“Show and Tell”). She also provides exhaustive lists of sets, costumes, and props for each play. Most stories require quite a few props, but these are often indicated as optional, and could be scavenged, made into a craft project, or conjured via imagination. Watts’ supporting documentation (lists, guidance, and recommendations) lessens the effort involved with getting each play “up on its feet.” This enables everyone involved to focus on the excitement of stories and vocal participation. It’s not likely that anybody will say “No, no, no, no, no” to Munsch at Play Act Two. Highly Recommended: 4 out of 4 starsReviewer: Sarah PolkinghorneSarah is a Public Services Librarian at the University of Alberta. She enjoys all sorts of books.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".