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Record W2917497111 · doi:10.1038/s41598-019-39600-9

Peer effects on control-averse behavior

2019· article· en· W2917497111 on OpenAlexfundno aff
Sarah Rudorf, Thomas Baumgärtner, Daria Knoch

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersSaskatoon City Hospital Foundation
KeywordsControl (management)Stochastic gameIncentiveSocial psychologyPsychologyOutcome (game theory)PhenomenonMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

The urge to rebel against external control affects social interactions in many domains of our society with potentially far-reaching consequences. Nevertheless, it has remained unclear to what degree this control-averse behavior might be influenced by the people in our surroundings, our peers. In an experimental paradigm with real restrictions of the subjects' freedom of choice and no systematic incentives to follow the peer, we are able to demonstrate both negative and positive peer effects on control-averse behavior. First, we find that information about a peer's strongly control-averse behavior, although irrelevant for the subjects' outcome, increases the subjects' individual control-averse behavior. Second, we find that information about a peer's more generous and only weakly control-averse behavior increases subjects' generous behavior, even though it is associated with greater costs for the subjects. Critically, each subject's behavior determined the monetary payoff of both the subject and a third person, thereby constituting a social behavior with actual consequences. Interestingly, these peer effects are not moderated by self-assessments of the general resistance to peer influence or the general tendency to rebel against restrictions of one's freedom of choice. Contributing new insights into a complex and highly relevant social phenomenon, our results indicate that information about a single peer's behavior can influence individual control-averse behavior.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.017
GPT teacher head0.312
Teacher spread0.295 · 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 designNot applicable
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

Citations2
Published2019
Admission routes1
Has abstractyes

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