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Record W2340789001 · doi:10.20361/g2z31r

George by A. Gino

2016· article· en· W2340789001 on OpenAlexvenueaboutno aff
Emily Paulsen

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)GirlArt historyArtLiteraturePhilosophyPsychoanalysisPsychology

Abstract

fetched live from OpenAlex

Gino, Alex. George. New York: Scholastic Press, 2015. Print.George is a bright, funny, and somewhat shy girl. Her main problem? Everyone thinks she’s a boy. She looks like a boy, she was born with all the parts of a boy, but she knows that she is a girl. She believes that she will have to hide her true self forever until the chance to play Charlotte in the school’s production of Charlotte’s Web arises and, with the support of her best friend Kelly, changes everything.There are few characters in George that perform with complete virtuosity or complete viciousness. Instead, Gino presents a cast that reflects our own society: some are confused, some say the wrong things, some are cruel, some supportive, and some just worry about George’s safety as she moves into a frequently victimized population. Gino’s prose throughout the novel refers to George as “her.” This pronoun choice in addition to the powerful insight that readers gain from seeing into the mind of George, make it clear that George simply is a girl. Gino does excellent work to create this connection and understanding between George and the reader so that the reader can feel how wrong it is when George is treated as if she’s a boy. The incessant gendering of everyday life is apparent and absurd when it is forced upon George. People, being such visual animals, often focus on the appearance of transgender individuals or can find it difficult to reconcile seeing a “boy” and being told that they are actually a girl inside. But because George’s story has taken the form of the written word, we are not so distracted by what we see and we can instead be more open to understanding.Not just for those questioning their own gender identity, this novel works to inform and inspire empathy for all readers. It is an absolute necessity for a collection that strives for diverse representation.Highly recommended: 4 out of 4 starsReviewer: Emily PaulsenEmily Paulsen is recent graduate from the School of Library and Information Studies Master’s program at the University of Alberta. She is born and raised in Edmonton and enjoys travelling, food, and photography.

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: Empirical · Consensus signal: none
Teacher disagreement score0.167
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1670.149

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 head0.006
GPT teacher head0.219
Teacher spread0.214 · 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
GenreEmpirical

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 routes2
Has abstractyes

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