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Record W602026848 · doi:10.1037/e507332013-001

The Structure of Cultural Orientations to the Good Life and their Expression in Personal Narratives

2012· dataset· en· W602026848 on OpenAlexaffabout
Gregory Bonn

Bibliographic record

VenuePsycEXTRA Dataset · 2012
Typedataset
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativeExpression (computer science)PsychologyAestheticsSocial psychologyLiteratureArtComputer science

Abstract

fetched live from OpenAlex

Understanding the rational and ethical sense that people make of their actions and experiences requires understanding the lives they are trying to live. The narrative visions of a good life that are dominant in a society thus represent an important aspect of cultural orientation. To gain insight into the form and function of these visions, two studies were conducted. In Study 1, the various criteria by which people judge their lives as good or worthy were examined using multidimensional scaling of responses from four different cultural groups of students: Chinese, East Asian Canadian, South Asian Canadian, and Western European Canadian. The results revealed two underlying structural dimensions on which both criteria and cultural groups could be differentiated. One reflected the locus of criterial goods and the other their morality. The clearest cultural contrast was between Chinese participants, who tended toward the prudential, materialistic, and hedonistic pole of the morality dimension, and South Asians, who tended more toward the spirituality and beneficence pole. In Study 2, the content of personal narratives produced by Chinese and South Asian students was analyzed to examine whether their contrasting orientations to the good life would be reflected in the kinds of life experiences they recounted. Some evidence of correspondence in this regard was found.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.031
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.389
Teacher spread0.316 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2012
Admission routes2
Has abstractyes

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