Bibliographic record
Abstract
Stanton, Brandon. Little humans. New York, NY: Farrar Straus Giroux Books for Young Readers, 2014. PrintFrom the creator of Humans of New York, comes the most dynamic, colourful and diverse group of little people of New York City. Brandon Stanton’s ability to capture the unique personalities of his subjects and “tell” a story through pictures is both captivating and brilliantly vivid. The ‘Little Humans’ are shown throughout the book in bright close-ups and even brighter clothing. Each page is filled in entirely with a close-up photograph of a child in different situations. Cultural diversity and differences are prominent in the photographs, but the text tackles the sameness we all share.“Little humans can be tough…but not too tough to need a hug.”Set against New York City streets, Little Humans embodies the ethnic diversity of the people of NYC. The text is limited, but has themes of resiliency, strength, identity and character woven throughout. Stanton writes of how little people are strong, talented and helpful.The story itself could have been written with more depth; however, the lack of narrative allows the reader to engage with the photographs and imagine the story of each unique person that is highlighted.Highly Recommended: 4 out of 4 starsReviewer: Kerri TrombleyKerri Trombley is a Vice Principal with Sturgeon School Division and is currently completing her Master’s Degree in Elementary Education at the University of Alberta. She shares her love of literature with all of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.140 |
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".