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Record W2750995011 · doi:10.1111/bjso.12216

Low relational mobility leads to greater motivation to understand enemies but not friends and acquaintances

2017· article· en· W2750995011 on OpenAlexaff
Liman Man Wai Li, Takahiko Masuda, Hajin Lee

Bibliographic record

VenueBritish Journal of Social Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Alberta
FundersMinistry of Education of the People's Republic of China
KeywordsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Enemyship occurs across societies, but it has not received as much attention as other types of relationships such as friendship in previous research. This research examined the influence of relational mobility on people's motivation to understand their personal enemies by measuring different dependent variables across three studies. First, a cross-cultural comparison study found that Hong Kong Chinese, from a low-relational-mobility society, reported a stronger desire to seek proximity to enemies relative to European Canadians, from a high-relational-mobility society (Study 1). To test causality, two manipulation studies were conducted. Participants were presented with images of co-workers, including enemies, friends, and acquaintances, in a hypothetical company. The results showed that the participants who perceived lower relational mobility paid more attention to their enemies in an eye-tracking task (Study 2) and had a higher accuracy rate for recognizing the faces of the enemies in an incidental memory test (Study 3). In contrast, the influence of relational mobility on motivation to understand friends and acquaintances was minimal. Implications for research on interpersonal relationships and relational mobility are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.380
Teacher spread0.305 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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