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Record W2791601693 · doi:10.1037/pspi0000119

Motivation moderates the effects of social support visibility.

2018· article· en· W2791601693 on OpenAlexaff
Katherine S. Zee, Justin V. Cavallo, Abdiel J. Flores, Niall Bolger, E. Tory Higgins

Bibliographic record

VenueJournal of Personality and Social Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPsycINFOPsychologySocial supportVisibilitySocial psychologyAction (physics)Cognitive psychologyMEDLINE

Abstract

fetched live from OpenAlex

Social support can sometimes have negative consequences for recipients. One way of circumventing these negative effects is to provide support in an 'invisible' or indirect manner, such that recipients do not construe the behavior as a supportive act. However, little is known about how recipients' motivational states influence when visible (direct) support or invisible support is more beneficial. Using the framework of Regulatory Mode Theory, we predicted that recipients motivated to engage in critical evaluation (i.e., those with a predominant assessment motivation) would find invisible support more beneficial than visible support, whereas recipients motivated to initiate action (i.e., those with a predominant locomotion motivation) would find visible support more beneficial than invisible support. Findings from one 2 × 2 experiment (Study 1), two laboratory experiments (Studies 2-3), one dyadic study involving support conversations between friends (Study 4), and a meta-analysis aggregating data from all four studies supported these hypotheses. As predicted, support outcomes were better for assessment predominant recipients following invisible support, but were better for locomotion predominant recipients following visible support. Results indicate that support attempts could be made more effective by considering both support visibility and recipient motivation. (PsycINFO Database Record

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.088
GPT teacher head0.457
Teacher spread0.369 · 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

Citations39
Published2018
Admission routes1
Has abstractyes

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