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Record W2527989288 · doi:10.20361/g2kc8r

The Fault in Our Stars by J. Green - 2nd review

2016· article· en· W2527989288 on OpenAlexvenueno aff
Kiara BCR

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldPsychology
TopicJungian Analytical Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsDisappointmentGirlArt historyHistoryArtPsychologySocial psychology

Abstract

fetched live from OpenAlex

Green, John. The Fault in Our Stars. New York: Penguin Books, 2014. Print.[Spoiler Alert!]The name of my book is The Fault In Our Stars. The author is John Green and the publisher is the Penguin Group in 2014. My book is about a girl named Hazel Grace and she is sixteen years old. Hazel has cancer in her lungs and she meets a boy in her support group his name is Augustus Waters. Augustus had cancer but he got his cancer taken away from taking his leg but later on he gets cancer again! Augustus and Hazel fall in love but they don’t get to spend a lot of time together because Augustus's cancer makes him die!I liked that at some points in the book when it’s getting to sad it will get really funny! Or when the book seems real in my mind! I like that the book takes me to a whole new worldI didn’t like that Augustus dies right when things in life where getting good for them! I did not like that when Hazel goes to the place she dreamed going and to meet the guy who was there to tell her what it was about was a disappointment because the guy was mean and didn’t want to help her!My rating for the book would be four out of five.I would recommend this book to kids that are older than me because there are lots of hard words and some material that is hard to understand!Highly Recommended: 4 out of 5 starsReviewer: KiaraMy name is name Kiara and my favorite books to read are mystery and horror. My favorite book to read is miss peregrine's home for peculiar children because it is a bit of a mystery and a little bit of horror in the book. I think that reading is important because it helps you understand and read things!

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Review · Consensus signal: Review
Teacher disagreement score0.075
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.327
Teacher spread0.317 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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