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Record W4285005155 · doi:10.1037/apl0000580

Biting the hand that feeds: A status-based model of when and why receiving help motivates social undermining.

2022· article· en· W4285005155 on OpenAlexaff
Kenneth Tai, Katrina Jia Lin, Catherine K. Lam, Wu Liu

Bibliographic record

VenueJournal of Applied Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsycINFOPsychologyHarmSocial psychologyPerspective (graphical)Social statusMEDLINE

Abstract

fetched live from OpenAlex

Social exchange theory suggests that after receiving help, people reciprocate by helping the original help giver. However, we propose that help recipients may respond negatively and harm the help giver when they perceive helping as a status threat and experience envy. Integrating the helping as status relations framework and the social functional perspective of envy, we examine when and why receiving help may prompt help recipients to undermine help givers. Across four studies, we find progressive support for our results, which show that when individuals receive task-related help from help givers who are perceived to be more, rather than less, competent than them, they experience greater status threat and envy. As help recipients experience envy toward help givers, they are likely to undermine help givers, and this positive relationship becomes stronger for help recipients who have higher status striving motivation. Our findings underscore the status dynamics implicated in helping interactions by highlighting that help recipients, especially those with higher status striving motivation, may paradoxically undermine help givers when they perceive status threat from and feel envious of help givers, as a result of receiving help from more competent help givers. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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)0.000
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 score0.420
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.151
GPT teacher head0.372
Teacher spread0.220 · 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

Citations41
Published2022
Admission routes1
Has abstractyes

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