Bibliographic record
Abstract
Cortez, Brenda E. My Mom is Having Surgery (A Kidney Story). Donate Life, 2015. Based on the true story of author Brenda E. Cortez’s kidney transplant, My Mom is Having Surgery (A Kidney Story) describes the process of donating a kidney and offers encouragement for others to take steps and donate. In targeting her book at young readers, those who are just beginning to read independently, Cortez offers an educational overview of the process in the hopes of normalizing lifesaving procedures such as living organ donation. From reassuring her children, to explaining in detail why donating a kidney is the right choice, to the process of surgery, recovery, and returning home, My Mom is Having Surgery (A Kidney Story) offers a realistic look at the many stages of this procedure. Aimed at audiences who are skilled and capable of reading independently, My Mom is Having Surgery is written in paragraph format with a medium sized font. By following the mother's surgery through the eyes of her daughter, the book engages with children and shows them how to be both encouraging and how to cope with the difficulties faced by a parent undergoing this procedure. Each page of the book is accompanied by colour images which represent the activities described in the text. These images are aesthetically pleasing and would help solidify the message of the book for young readers. As a tool designed to change attitudes in an engaging way, this book is a must read for young children. Highly Recommended: 4 out of 4 starsReviewer: Madeline C. Crichton Madeline Crichton is a University of Alberta undergraduate student with a lifelong passion for reading. When she is not preoccupied with her studies, Madeline is busy volunteering in a variety of roles in her community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".