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Record W4405912908 · doi:10.1177/01461672241295500

Are Mediterranean Societies “Cultures of Honor?”: Prevalence and Implications of a Cultural Logic of Honor Across Three World Regions

2024· article· en· W4405912908 on OpenAlexaff
Vivian L. Vignoles, Alexander Kirchner‐Häusler, Ayşe K. Üskül, Susan E. Cross, Rosa Rodríguez‐Bailón, Isabella R. L. Bossom, Vanessa A. Castillo, Meral Gezici-Yalçın, Charles Harb, Keiko Ishii, Panagiota Karamaouna, Konstantinos Kafetsios, Evangelia Kateri, Juan Matamoros‐Lima, Rania Miniesy, Jinkyung Na, Zafer Özkan, Stefano Pagliaro, Charis Psaltis, Dina Rabie, Manuel Teresi, Yukiko Uchida, Michael J. A. Wohl

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCarleton University
FundersH2020 European Research Council
KeywordsHonorSocial psychologyPsychologySociology

Abstract

fetched live from OpenAlex

Mediterranean societies are often labeled as “honor cultures,” in contrast with presumed “dignity” and “face” cultures of Anglo-Western and East Asian societies. We measured these cultural logics in two large-scale surveys (Studies 1 & 3: N = 2,942 students from 11 societies; Study 2: N = 5,471 adults from 14 societies). Middle Eastern and North African groups perceived honor values as the most normative in their societies, followed by Southeast European, and then Latin-European groups (who were comparable to Anglo-Western and East-Asian groups). East-Asian and Anglo-Western groups, respectively, perceived face and dignity values as most normative. Culture-level variation in perceived normative honor values, but not personal values, accounted for previously reported differences between Mediterranean and non-Mediterranean samples in several (but not all) measures of social cognitive tendencies. We conclude that a cultural logic of honor plays a role in Mediterranean societies, but labeling these societies as “honor cultures” is oversimplistic.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.819

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.176
GPT teacher head0.442
Teacher spread0.266 · 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 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
Published2024
Admission routes1
Has abstractyes

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