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Record W4288385082 · doi:10.1037/xge0001259

The moralization of effort.

2022· article· en· W4288385082 on OpenAlexafffund
Jared Celniker, Andrew Gregory, Hyunjin J. Koo, Paul K. Piff, Peter H. Ditto, Azim Shariff

Bibliographic record

VenueJournal of Experimental Psychology General · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyValue (mathematics)PsycINFOSocial psychologyMoralityIrrationalityProduct (mathematics)DonationProsocial behaviorTraitRationalityEpistemologyEconomicsLaw

Abstract

fetched live from OpenAlex

= 5,502) demonstrate the nature of these effects in the domains of paid employment, personal fitness, and charitable fundraising. The exertion of effort is deemed morally admirable (Studies 1-6) and is monetarily rewarded (Studies 2-6), even in situations where effort does not directly generate additional product, quality, or economic value. Convergent patterns of results emerged in South Korean and French cross-cultural replications (Studies 2b and 2c). We contend that the seeming irrationality of valuing effort for its own sake, such as in situations where one's efforts do not directly increase economic output (Studies 3-6), reveals a "deeply rational" social heuristic for evaluating potential cooperation partners. Specifically, effort cues engender broad moral trait ascriptions, and this moralization of effort influences donation behaviors (Study 5) and cooperative partner choice decision-making (Studies 4 and 6). In situating our account of effort moralization into past research and theorizing, we also consider the implications of these effects for social welfare policy and the future of work. (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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.099
GPT teacher head0.361
Teacher spread0.261 · 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 designBench or experimental
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

Citations60
Published2022
Admission routes2
Has abstractyes

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