Bibliographic record
Abstract
Ahmed, Roda. Mae Among the Stars. Illustrated by Stasia Burrington, Harper Collins, 2018. Mae Among the Stars is the perfect picture book for any young child (3-8) interested in space. It tells the real-life story of Mae Jemison, the first African American woman in space. This story urges children to explore even the most impossible dreams, or as Mae’s parents repeatedly encourage throughout: “If you can dream it, if you believe it and work hard for it, anything is possible.” The story revolves around the age-old question most of us are asked as children: What would you like to be when you grow up? Mae Jemison dreams about being an astronaut. Although her teacher tries to deter her from pursuing this dream, Mae refuses to give up. Thanks to her determination and parents’ reinforcement, she continues to work towards achieving her “impossible” dream of seeing Earth from space. Kids will find the last page of this book particularly interesting because it contains the bio of Mae Jemison and her accomplishments. The illustrations in this book elevate the story to an exceptional level. The rich colours and imaginative drawings bring each page to life. The illustrations are so vivid that one page in particular stands out from the rest because it is depicted in a muted blue, representing Mae’s gloomy response to her teacher’s disapproval of her dream of becoming an astronaut. Although the story tackles the deep-rooted issues of racial and gender stereotypes, the writing is simple enough for young readers to connect with Mae’s story while still inspiring them to reach for the stars. Recommended: 3 out of 4 starsReviewer: Bridget Harty Bridget Harty is a University of Alberta undergraduate student in the Elementary Education program. She enjoys spending time with family and friends and rereading the Harry Potter series any chance she gets.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.151 | 0.144 |
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".