MétaCan
Menu
Back to cohort
Record W6901675590 · doi:10.60692/m1dv3-qvw25

Unpackaging the link between economic inequality and self-construal

2023· article· en· W6901675590 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCarleton University
Fundersnot available
KeywordsInequalityEconomic inequalityChristian ministryFeelingPromotion (chess)Economic scienceSocial inequality

Abstract

fetched live from OpenAlex

ABSTRACTPast research has shown that economic inequality shapes individuals' self-construals. However, it has been unclear which dimensions of self-construal are associated with and affected by economic inequality. A correlational (Study 1: N = 264) and an experimental study (Study 2: N = 532) provided converging evidence linking perceived economic inequality with two forms of independent (vs. interdependent) self-construal: Difference from Others and Self-Reliance. In Study 3 (N = 12,634) societal differences in objective economic inequality across 48 nations predicted feelings of Difference from Others, but not Self-Reliance. Importantly, we found no significant associations of economic inequality with the other six dimensions of self-construal. Our findings help extend previous results linking economic inequality to forms of "social distance."KEYWORDS: Economic inequalitymultidimensional self-construalindependenceinterdependence Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical statementThe ethical committee approved the reported studies of the University of Kent (Study 1, Ethics Clearance ID: 201715059194974550) and the University of Granada (Study 2: Ethics Clearance ID 170/CEIH/2016). Study 3 was approved by the research ethics committee of the Institute of Psychology of the Polish Academy of Science (approval #7/11/2017); additionally, in each country where local regulations require separate ERB approval, local teams obtained such approvals.Supplementary dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/15298868.2023.2200032.Additional informationFundingThe present research was supported by a fellowship from the Spanish Ministry of Education, Culture, and Sport (FPU-13/01231), grants from the Spanish Ministry of Economy and Competitiveness (PSI2016–78839-P and PID2019.105643GB.I00), by the Polish National Science Centre under Grant 2020/37/B/HS6/03142; the Japan Society for the Promotion of Science under Grants P17806 and 17F17806; the Hungarian OTKA under Grant OTKA-K 135963; the Brazilian National Council for Research—CNPq under Grant PQ301298/2018-1; the Czech Science Foundation under grant 20-08583S; the Shota Rustaveli National Science Foundation of Georgia under grant number YS 17–43; the National Natural Science Foundation of China under grant 71873133; and the Department of Educational Studies, University of Roma Tre under biannual Grant DSF 2017-2018.

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)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.115
GPT teacher head0.308
Teacher spread0.193 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueGreater South Information SystemSame topicCultural Differences and ValuesFrench-language works237,207