Bibliographic record
Abstract
Jiani. Cicadas’ Singing. Edmonton: Gold Mum Publishing, 2011. Print. Cicadas’ Singing was published last year by newly established Gold Mum Publishing, which specializes in bilingual books for children. I had been taking Mandarin lessons from a private tutor recently, so this seemed like an ideal opportunity to review a book with English, Chinese characters, and pinyin (the system to transliterate Chinese characters into the Latin alphabet). The story is about a girl named Huahua who lives in 1960s China with her divorced mother. We learn that this is a difficult time to be divorced because divorced women were thought to be “dirty”, but no reason or context is provided to help readers understand this form of discrimination. Following the word “dirty” is a clumsy transition to a description of Grandma Du, who is introduced as a garbage collector; she apparently is also considered dirty, “but not in the same way”. Huahua develops a friendship with Grandma Du and they begin spending time playing with homemade toys and singing songs. Knowing that Huahua desperately wants to go to kindergarten, Grandma Du offers her some advice: “Huahua is a pretty girl and will marry a wealthy man.” Once again, the story introduces to us a cultural curiosity that begs further description, nuance, or context, but instead the story makes another rough transition, this time to the day when Huahua is old enough to attend kindergarten. Huahua goes off to school and starts to avoid her older friend because “others thought Grandma Du was dirty”. Huahua pretends not to know Grandma Du, so they no longer see each other and soon thereafter we learn that Grandma Du has died. Huahua attends the funeral with her mother where they notice an unusually large number of singing cicadas, hence the title. The first problem with this book is the story, which will likely be difficult and unsatisfying for young readers. The English text has a number of problems with punctuation, diction, and grammar, and the brief cultural notes in the appendix are woefully inadequate to explain many of the cultural references. The illustrations have a certain appeal, but they are too often overwhelmed by dense layers of text. Here’s hoping that this series improves with time. Not recommended: 1 out of 4 starsReviewer: Robert DesmaraisRobert Desmarais is Head of Special Collections at the University of Alberta and Managing Editor of The Deakin Review of Children’s Literature. A graduate of the University of Toronto’s Faculty of Information Studies, with a Book History and Print Culture designation, he also has university degrees in English literature and publishing. He has been collecting and enjoying children’s books for as long as he can remember.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.196 | 0.104 |
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".