Bibliographic record
Abstract
Bailey, Linda. Mary Who Wrote Frankenstein. Illustrated by Júlia Sardà. Tundra Books, 2018. The subject of this book is the story-behind-the-story of Dr Frankenstein and his terrible creature. It is the story of how an 18-year-old girl came to write one of the best-known novels of all time, inspired by a friendly competition among a group of friends that included two of the most famous authors of the age (Percy Bysshe Shelley and Lord Byron). While on summer vacation in 1816, staying at Villa Diodati (a mansion on the shores of Lake Geneva), a dark and stormy night offered the ideal setting for Lord Byron to read aloud from a book of ghoulish stories called Fantasmagoriana and inspired him to propose a friendly competition in which each member of the assembled party would write a ghost story. Although it would take her almost a year to complete the novel, this competition was the beginning of Mary Shelley’s Frankenstein. In a preface to the third edition, written many years later, the author describes these events to explain how a young girl “came to think of and to dilate upon so very hideous an idea.” Inspired by Mary Shelley’s preface, Linda Bailey has reimagined this well-known history in a way that is very relatable for young readers. Bailey explores some of the formative moments in Mary’s childhood—in particular, the death of her mother when she was only 11 days old—and some of recent scientific discoveries that were challenging old ideas, all to explain how Mary could imagine a creature “made of dead body parts, stretched out—and coming to life.” While not at all suitable for very young or very sensitive readers, this story will be inspiring for some. Perhaps the best part of the book, the gorgeously stylized illustrations by Júlia Sardà are powerfully evocative. Like the text, the illustrations are darkly appropriate reflections of Mary’s story without being overly gruesome. This is a wonderful book for the right reader. Tundra is marketing this book as being suitable for children aged 5-8, but the subject matter will be too dark for many in this age group. Somewhat older children may be better equipped to deal with the difficult elements of the story and they may be interested in the research notes at the back of the book. Highly recommended: 4 out of 4 starsReviewer: Linda Quirk Linda taught courses in Canadian Literature, Women's Writing, and Children's Literature at Queen's University (Kingston) and at Seneca College (Toronto) before moving to Edmonton to become a librarian at University of Alberta’s Bruce Peel Special Collections.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.090 |
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".