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Record W1911392769 · doi:10.1037/pspp0000058

I don’t want the money, I just want your time: How moral identity overcomes the aversion to giving time to prosocial causes.

2015· article· en· W1911392769 on OpenAlexaff
Americus Reed, Adam Kay, Stephanie Finnel, Karl Aquino, Eric P. Levy

Bibliographic record

VenueJournal of Personality and Social Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProsocial behaviorPsychologySocial psychologySalience (neuroscience)Moral developmentIdentity (music)Moral reasoningTime preferenceMoral disengagementPersonal identityPsycINFOSelf-conceptEconomicsCognitive psychologyMicroeconomicsLaw

Abstract

fetched live from OpenAlex

Four studies show that moral identity reduces people's aversion to giving time-particularly as the psychological costs of doing so increase. In Study 1, we demonstrate that even when the cost of time and money are held equivalent, a moral cue enhances the expected self-expressivity of giving time-especially when it is given to a moral cause. We found that a moral cue reduces time aversion even when giving time was perceived to be unpleasant (Study 2), or when the time to be given was otherwise seen to be scarce (Study 3). Study 4 builds on these studies by examining actual giving while accounting for the real costs of time. In this study, we found that the chronic salience of moral identity serves as a buffer to time aversion, specifically as giving time becomes increasingly costly. These findings are discussed in terms of the time-versus-money literature and the identity literature. We also discuss policy implications for prosocial cause initiatives. (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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.202
GPT teacher head0.423
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations108
Published2015
Admission routes1
Has abstractyes

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