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Record W4387818233 · doi:10.5430/wjel.v13n8p566

Green Book Revisited: Unpacking the Complexities of Race and Friendship

2023· article· en· W4387818233 on OpenAlexvenueno aff
Elizabeth Anggraeni Amalo, Eny Kusumawati, Irwan Sumarsono, Imam Dui Agusalim, Radina Anggun Nurisma, Diana Budi Darma, Adolfina M. S. Moybeka

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Sociology, Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFriendshipUnpackingRace (biology)RacismRepresentation (politics)SociologyCritical race theoryGender studiesSocial scienceLinguisticsPolitical scienceLawPoliticsPhilosophy

Abstract

fetched live from OpenAlex

This study intends to investigate how friendship and race are portrayed in Peter Farrelly's Green Book movie. The study inquires whether the film depicts racism simplistically or accurately depicts the relationship between individuals from different racial backgrounds. This study's importance rests in its potential to advance the current dialogue regarding race and representation in popular culture. To respond to the research question, the researchers gathered primary sources of information from the screenplay and reviews of the movie Green Book, which were then analyzed qualitatively to identify and explore themes and characters related to race and friendship. In addition, the researchers gathered secondary data from online journals of English literature, e-books, and other sources. The information was then categorized, examined, debated, and presented to the readers. In this paper, the researchers include the historical and sociological contexts of the movie, the lives of Dr. Shirley and Tony Lip, and racism in America. The application of Critical Race Theory enriches the discussion of the movie. The study found that Green Book depicts the gradual close relationship between two different individuals from different racial backgrounds and the simplified portrayal of racism.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.069
GPT teacher head0.388
Teacher spread0.319 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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