MétaCan
Menu
Back to cohort
Record W2593640885 · doi:10.1177/0308275x17694943

Virtuous power: Ethics, Confucianism, and Psychological self-help in China

2017· article· en· W2593640885 on OpenAlexafffund
Jie Yang

Bibliographic record

VenueCritique of Anthropology · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaLondon School of Economics and Political Science
KeywordsVirtue ethicsSociologySocial psychologyVirtuePsychologyLawPolitical science

Abstract

fetched live from OpenAlex

This article examines a genre of psychological self-help in China that deploys Confucian ethics to address social, moral, and psychological distress. Within this genre, a branch of what is called “third force” self-help, which attempts to overcome an ambiguous “third state” between health and illness, advocates encourage individuals to cultivate a form of virtuous power that emanates from the heart, seen as the basis of cognition, virtue, and bodily sensation. The heart has the freedom to imagine and act but also constrains such freedom. It constitutes the moral core necessary for achieving equanimity, a state of equilibrium in which one is not shaken by external disturbances and spontaneous bodily reactions are regulated by high moral reflection. This third force self-help uses heart-based Confucian ethics not only to help individuals cope with socioeconomic changes, but also, I argue, to constrain direct opposition to the causes of those changes by translating structural inequalities into ethical and moral issues. I suggest that this virtuous power serves government interests. The emphasis on Confucian ethics humanizes market competition and biologizes individual and family responsibility for care, legitimizing both class stratification and the family as a provider of social welfare.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.236
GPT teacher head0.543
Teacher spread0.307 · 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

Citations19
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCritique of AnthropologySame topicMental Health and Patient InvolvementFrench-language works237,207