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Record W2483529985 · doi:10.20361/g27618

The Beautiful and the Cursed by P. Morgan

2016· article· en· W2483529985 on OpenAlexvenueaboutno aff
Colette Leung

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBrotherSisterDaughterIdentity (music)FantasyArtArt historyGenealogyHistoryLawLiteraturePolitical science

Abstract

fetched live from OpenAlex

Morgan, Page. The Beautiful and the Cursed. Toronto: Doubleday Canada, 2013. Print.This young adult fantasy novel tells the story of a young woman named Ingrid Waverly. Ingrid is a seventeen-year-old living in London, England at the end of the 19th century. She is the daughter of a wealthy man. However, after becoming the heart of a scandal involving a mysterious fire, Ingrid moves with her mother and little sister to Paris, France. Ingrid’s twin brother Grayson has already purchased an old abbey there for the family to settle into. Ingrid’s mother intends to restore the abbey, and turn it into a gallery to showcase her art. Once in Paris, however, Ingrid quickly becomes steeped in a supernatural world.Upon arrival, Ingrid learns that young women have been going missing in Paris, as has her twin brother Grayson. Some of the missing women have been found dead and mutilated. Although her mother and the police seem unconcerned and believe Grayson is just gallivanting about the city, Ingrid has a deep connection with her brother and knows something is wrong. With the help of her impetuous younger sister Gabby, Ingrid begins investigating her brother’s disappearance. Gabby is also trying to discover her own identity as a young woman, and forge a deeper relationship with Ingrid, who has always been closer to Grayson. Through their investigation, the sisters quickly uncover a secret world of fallen angels, demons, and hellhounds, not least because the Waverly family is protected by a gargoyle.One of the statues of the old abbey is actually a gargoyle named Luc, who is duty bound by angels to protect the family living in the abbey. He disguises himself as a servant, but his true form is that of a stone monster. Although tasked to protect the entire family, Luc finds himself increasingly drawn to Ingrid, and develops feelings for her. Grayson’s disappearance is tied to Luc’s secret world. Grayson was kidnapped by a fallen Angel, and is being tortured with hellhound blood injections. Reluctantly, Luc becomes involved with the sisters’ quest. They are helped by the Alliance, a secret demon fighting organization. It becomes apparent that Grayson was kidnapped because he has special abilities, as does Ingrid, explaining her role in the scandal that forced her to leave London. The sisters must unravel this new world, their roles in it, and save their brother in time.The Beautiful and the Cursed is told in multiple viewpoints, which may deter some readers, especially when viewpoints change within the same chapter, or describe a repeated scene. It is the first book of a trilogy. The book’s mythology is well explained in approachable language. Morgan draws influence from both the Mortal Instruments series and the 1990s Gargoyles television show, but the book holds its own as original, and will appeal to female demographics. Themes explored include death, torture, forbidden romance, and a fantastical twist on angels and demons.Recommended: 3 out of 4 starsReviewer: Colette LeungColette Leung is a graduate student at the University of Alberta, working in the fields of Library and Information science and Humanities Computing who loves reading, cats, and tea. Her research interests focus around how digital tools can be used to explore fields such as literature, language, and history in new and innovative ways.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0050.005
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0440.021

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.

Opus teacher head0.004
GPT teacher head0.209
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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