Bibliographic record
Abstract
Martinez-Neal, Juana. Alma and How She Got Her Name. Candlewick Press, 2018. Alma and How She Got Her Name is the perfect book for any child who questions the meaning behind his or her name. In Juana Martinez-Neal’s first picture book, she tells the beautiful story of a young girl who wonders why her name is so long. Alma Sofia Esperanza Jose Pura Candela slowly hears the special meaning behind each one of her names from her Dad and begins to see how well her name fits her. Alma learns about her ancestors and how they can be role models in her life and how their personalities are reflected in her. Martinez-Neal has created stunning illustrations using graphite and coloured pencils that evoke a calming mood and complement the theme of family connection and intimacy. The author makes excellent use of descriptive, expressive language to tell this story. The words and pictures work together to emphasize the importance of family and finding a sense of belonging. This story is a perfect way for parents to start a conversation with their children about their name and their ancestors. This book would also make a great resource in the classroom as it celebrates cultural diversity and it will send a powerful message to students with names that are often mispronounced or made fun of. Children will learn to feel proud about who they are and what their name means. Juana Martinez-Neal shares the story of the meaning behind her own name at the end of the book and invites the reader to do the same. Highly Recommended: 4 out of 4 stars Reviewer: Jill Brown Jill Brown is currently in her fourth year of the B.Ed. Elementary program at the University of Alberta. She has had a passion for reading since an early age and she looks forward to sharing that passion with her future elementary students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.015 |
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".