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Record W4404845567 · doi:10.1177/08902070241298850

Wherefore art thou competitors? How situational affordances help differentiate among prosociality, individualism, and competition

2024· article· en· W4404845567 on OpenAlexafffund
Yi Liu, Adam W. Stivers, Ryan O. Murphy, Niels J. Van Doesum, Jeff Joireman, Marcello Gallucci, Efrat Aharonov‐Majar, Ursula Athenstaedt, Liying Bai, Robert Böhm, Nancy R. Buchan, Xiaoping Chen, Kitty Dumont, Jan B. Engelmann, Kimmo Eriksson, Hyun Euh, Susann Fiedler, Justin Friesen, Simon Gächter, Camilo García, Roberto González, Sylvie Graf, Katarzyna Growiec, Martina Hřebı́čková, Gökhan Karagonlar, Toko Kiyonari, Yu Kou, D. Michael Kuhlman, Siugmin Lay, Geoffrey J. Leonardelli, Norman P. Li, Yang Li, Boris Maciejovsky, Zoi Manesi, Ali Mashuri, Aurelia Mok, Karin S. Moser, Adrian Netedu, Chandrasekhar Pammi, Michael J. Platow, Chris Reinders Folmer, Cecilia Reyna, Cláudia Simão, Sonja Utz, Leander van der Meij, Sven Waldzus, Yiwen Wang, Bernd Weber, Ori Weisel, Tim Wildschut, Fabian Winter, Junhui Wu, Jose C. Yong, Paul A. M. Van Lange

Bibliographic record

VenueEuropean Journal of Personality · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of TorontoUniversity of Winnipeg
FundersFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasSocial Sciences and Humanities Research Council of CanadaCentro de Estudios de Conflicto y Cohesión SocialH2020 European Research CouncilAkademie Věd České RepublikyAgencia Nacional de Investigación y DesarrolloChina Scholarship CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekGrantová Agentura České Republiky
KeywordsPsychologyThouSituational ethicsAffordanceCompetition (biology)Competitor analysisSocial psychologyIndividualismCognitive psychology

Abstract

fetched live from OpenAlex

The Triple Dominance Measure (choosing between prosocial, individualistic, and competitive options) and the Slider Measure (“sliding” between various orientations, for example, from individualistic to prosocial) are two widely used techniques to measure social value orientation, that is, the weight individuals assign to own and others’ outcomes in interdependent situations. Surprisingly, there is only moderate correspondence between these measures, but it is unclear why and what the implications are for identifying individual differences in social value orientation. Using a dataset of 8021 participants from 31 countries and regions, this study revealed that the Slider Measure identified fewer competitors than the Triple Dominance Measure, accounting for approximately one-third of the non-correspondence between the two measures. This is (partially) because many of the Slider items do not afford a competitive option. In items where competition is combined with individualism, competitors tended to make the same choices as individualists. Futhermore, we demonstrated the uniqueness of competitors. Compared to prosocials and individualists, competitors exhibited lower levels of both social mindfulness and trust. Overall, the present work highlights the importance of situational affordances in measuring personality, the benefits of distinguishing between individualists and competitors, and the importance of utilizing a measure that distinguishes between these two proself orientations.

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)0.003
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 score0.499
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.308
Teacher spread0.274 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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