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Record W4392287276 · doi:10.1177/13634615241233682

Deconstructing wisdom through a cultural lens: Folk understandings of wisdom and its ontology in the Philippines and Sri Lanka

2024· article· en· W4392287276 on OpenAlexaff
Santushi D. Amarasuriya, María Guadalupe C. Salanga, Charisse Tan Llorin, Marie Rose H. Morales, Eranda Jayawickreme, Igor Grossmann

Bibliographic record

VenueTranscultural Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Waterloo
FundersTempleton World Charity Foundation
KeywordsHumilitySri lankaOpenness to experienceSociologyPoliticsCollectivismEpistemologyPhronesisEnvironmental ethicsPsychologySocial psychologyIndividualismPolitical scienceAnthropology

Abstract

fetched live from OpenAlex

In many contemporary societies, misinformation, epistemic arrogance, and intergroup conflict pose serious threats to social cohesion and well-being. Wisdom may offer a potential antidote to these problems, with a recently identified Common Wisdom Model (CWM) suggesting that wisdom involves epistemic virtues such as intellectual humility, openness to change, and perspective-taking. However, it is unclear whether these virtues are central for folk concepts of wisdom in non-Western contexts. We explored this question by conducting focus group discussions with 174 participants from the Philippines and Sri Lanka, two countries facing socio-political and economic challenges. We found that epistemic themes were common in both countries, but more so when participants were asked to define wisdom in general terms rather than to describe how it is acquired or expressed in daily lives. Moreover, epistemic themes were more prevalent among Filipino than Sri Lankan participants, especially when the questions posed were abstract rather than concrete. We discuss how these findings relate to the CWM and the socio-cultural contexts of the two countries, and suggest that a question format should be considered in cross-cultural research on wisdom.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.566

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.389
Teacher spread0.301 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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