Bibliographic record
Abstract
de la Peña, Matt. Love. Illustrated by Loren Long, G.P. Penguin Random House, 2018. Newbery Medal-winning author of Last Stop on Market Street Matt de la Peña and New York Times bestselling illustrator Loren Long have teamed up to paint a touching picture of where love lives in each of our lives. Perfect for the child in your life or the child in you, this book ignites a warm feeling deep down inside. Love shines through in all our senses. Through poetic verses we are reminded of what love looks like, what love sounds like, what love tastes like, what love smells like, and what love feels like, physically and emotionally. Who cannot relate to the simple innocence of laughing as you run through the sprinkler on a hot summer's day? One much-needed two-page spread even highlights the importance of self-love. The gorgeous illustrations are comprised of a compilation of monotype prints, acrylic paint, and pencil. They perfectly complement the words that they accompany, even telling their own hidden stories along the way. It is clear to understand how the pictures connect to the words, the two parts work together to invoke emotions in the reader. The vocabulary and structure, like how the word love is used in a variety of manners and how each page offers a glimpse into different individuals’ lives, may be challenging for younger children to read on their own but with the right facilitation any child can find connections to this beautiful picture book. Highly Recommended: 4 out of 4 starsReviewer: Bretton Bowd
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.152 |
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".