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Record W2478590837 · doi:10.3968/8496

A Study of American Individualism: Taking Friends as an Exampl

2016· article· en· W2478590837 on OpenAlexvenueno aff
Yang Song, Tingting Wang

Bibliographic record

VenueCross-cultural communication · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIdeological and Political Education
Canadian institutionsnot available
Fundersnot available
KeywordsIndividualismCulture of the United StatesSociologyChinaFeelingValue (mathematics)American literatureAmerican studiesAestheticsSocial psychologyGender studiesPsychologyPolitical scienceLawHumanitiesArtLiterature

Abstract

fetched live from OpenAlex

Friends are a famous American situation comedy since 1990s. It tells the story of six “common” youths living in New York of the United States including their emotion, career, joys and pains. Based on the study of Friends case, this article analyses the importance of American individualism to American culture, and tells us the reasons why individualism became the core of American culture. It also discusses the concrete embodiment of individualism value in American daily life from the life, career and feelings. This paper understands the importance of American individualism in American culture through current situations of American individualism. It helps us to understand deeply the impact and important significance on American culture which is caused by the American individualism. And helps us to better understand and look at the essence of American society and culture, understand the difference between USA and China. It also contributes a lot to promote cultural exchanges and common progresses between China and America and common progress.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0210.009
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.462
Teacher spread0.359 · 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 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
Published2016
Admission routes1
Has abstractyes

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