Bibliographic record
Abstract
Funk, Josh. How to Code a Sandcastle. Illustrated by Sara Palacios, Viking, 2018. How to Code a Sandcastle, written by Josh Funk, is endorsed as a “Girls Who Code” book. Founded in 2013, the mission of Girls Who Code is to close the gender gap in technology. One of the tools they use to help them reach their young female target audience is children’s literature. The story begins with the protagonist, Pearl, comically explaining a variety of challenges she encountered when building a sandcastle at the beach. Now, on the last day of her summer vacation, Pearl has decided to recruit the help of her robot, Pascal, to build her sandcastle. Pearl gives instructions to Pascal, using code, and explains the coding concepts of sequence, loop, and if-then-else to the reader as they relate to the task of building her sandcastle. These concepts are introduced and explained to the young readers in a fun and engaging manner. The text features of this book are eye catching and educational. Josh Funk strategically changes the text color to highlight coding vocabulary and he draws the reader’s attention to coding commands by changing the text font when Pearl is giving instructions to Pascal. Speech bubbles are used throughout the book in an appealing, non-linear manner. The illustrations are interesting, colourful, and visually captivating to all readers, young and old. The images are crafted with purpose as they enhance the reader’s ability to make meaning of the coding concepts by providing them with visual information to complement and reinforce the descriptions given in the text. The last two pages of the book, Pearl and Pascal’s Guide to Coding, give a more in-depth explanation of the coding concepts, providing extra support for children and adults who may be new to coding. This book is an entertaining and educational must read! Highly Recommended: 4 stars out of 4Reviewer: Dannielle Sehgal Dannielle Sehgal is a kindergarten teacher and graduate student at the University of Alberta. Dannielle is passionate about her career and loves discovering new picture books that she can bring to her classroom and read to her 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 | 0.000 | 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 | 0.002 | 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) | 0.053 | 0.035 |
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".