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Record W2741367981 · doi:10.1108/ssrp-05-2017-0021

The representations of LGBTQ themes and individuals in non-fiction young adult literature

2017· article· en· W2741367981 on OpenAlexaff
John H. Bickford

Bibliographic record

VenueSocial Studies Research and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsLesbianQueerGender studiesTranssexualTransgenderSociologyHuman sexualityValue (mathematics)

Abstract

fetched live from OpenAlex

Purpose Social justice themes permeate the social studies, history, civics, and current events curricula. The purpose of this paper is to examine how non-fiction trade books represented lesbian, gay, bisexual, transsexual, transgender, and queer (LGBTQ) individuals and issues. Design/methodology/approach Trade books published after 2000 and intended for middle grades (5-8) and high school (9-12) students were analyzed. Findings Findings included main characters’ demography, sexuality, and various ancillary elements, such as connection to LGBTQ community, interactions with non-LGBTQ individuals, the challenges and contested terrain that LGBTQ individuals must traverse, and a range of responses to these challenges. Publication date, intended audience, and subgenre of non-fiction – specifically, memoir, expository, and historical text – added nuance to findings. Viewed broadly, the books generally engaged in exceptionalism, a historical misrepresentation, of one singular character who was a gay or lesbian white American. Diverse sexualities, races, ethnicities, and contexts were largely absent. Complex resistance structures were frequent and detailed. Originality/value This research contributes to previous scholarship exploring LGBTQ-themed fiction for secondary students and close readings of secondary level non-fiction trade books.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.199
GPT teacher head0.596
Teacher spread0.397 · 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.

Study designObservational
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

Citations6
Published2017
Admission routes1
Has abstractyes

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