Bibliographic record
Abstract
Wright, Laurie. I Believe in Myself! Illustrated by Ana Santos, Laurie Wright, 2018. Laurie Wright is a childhood mental health specialist and this book is one of seven books within her Mindful Mantras collection. All the books are centered around BIG feelings and examples for creating positive self talk. These books are clearly designed to help both parents and teachers open the discussions regarding mental health with young children. The vocabulary and scenarios in this book are suitable for younger readers and listeners, and the book is quite accurately rated for ages 4 – 8. The theme is very well developed, in that after every scenario, there is a positive solution, and repeated phrase, “I believe in myself!” This book invites the readers and listeners to think about times they have been in similar scenarios and how they reacted when faced with them. A vast array of emotions are covered, from anxiety, to shyness, to nervousness, to panic, to frustration, and many more. This provides the children with a diversity of feelings to relate to. The illustrations in the book are a strong and accurate representation of what specific feelings could look like, which can help younger students learn social skills and the virtue of empathy. The book's format, font size, and images are big enough to be used for a group/class discussion, as well as one on one. The book also comes with free additional resources at the author’s website in regard to mental health, and how to talk to children about it. Overall, the book is a quick read aloud that is perfect for opening the door to start the conversations about BIG feelings, mental health, positive self talk and empathy. Highly Recommend: 4 out of 4 starsReviewer: Jessica Oscar
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.164 | 0.155 |
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".