Bibliographic record
Abstract
Fitzgerald, Juniper, and Elise Peterson. How mamas love their babies. The Feminist Press at the City University of New York, 2018. The first children’s book from sociology PhD Juniper Fitzgerald and artist-activist Elise Peterson, How Mamas Love Their Babies gently approaches an intersectional understanding of motherhood, while also connecting the many shared experiences of becoming and being a mother. Peterson creates a beautifully layered environment to accompany Fitzgerald’s bold, yet simple text. By overlaying colourful multimedia collage techniques with black and white retro photographs, the illustrations are dynamic and textured. The bright, engaging page design is inviting and makes this a wonderful selection for art educators looking for literacy tie-ins. This book is notable for its acknowledgment and celebration of the many ways mothers work, love and care for their babies. Fitzgerald draws upon her personal experiences labouring in the sex industry to bring forward a unique space within the text by including parents whose work may be stigmatized. In doing so, she underlines the importance of ensuring children can find themselves, their caregivers and communities represented respectfully within the pages of a book. A powerful, inclusive and decisively feminist addition to any children’s collection or storytime, Fizgerald and Peterson encourage readers to welcome, value and honour the presence of all mothers in the lives of their children and communities. Highly Recommended: 4 out of 4 stars Reviewed by: Alexandra Adams Alex is a busy mom, student and public library assistant, with a passion for Early Childhood Education and the Arts. She is currently working on her MLIS at the University of Alberta.
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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".