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Record W2907732843 · doi:10.1177/0956797618814145

People Are Slow to Adapt to the Warm Glow of Giving

2018· article· en· W2907732843 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsKellogg's (Canada)
FundersBooth School of Business, University of Chicago
KeywordsPsychologyCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

People adapt to repeated getting. The happiness we feel from eating the same food, from earning the same income, and from many other experiences quickly decreases as repeated exposure to an identical source of happiness increases. In two preregistered experiments ( N = 615), we examined whether people also adapt to repeated giving-the happiness we feel from helping other people rather than ourselves. In Experiment 1, participants spent a windfall for 5 days ($5.00 per day on the same item) on themselves or another person (the same one each day). In Experiment 2, participants won money in 10 rounds of a game ($0.05 per round) for themselves or a charity of their choice (the same one each round). Although getting elicited standard adaptation (happiness significantly declined), giving did not grow old (happiness did not significantly decline; Experiment 1) and grew old more slowly than equivalent getting (happiness declined at about half the rate; Experiment 2). Past research suggests that people are inevitably quick to adapt in the absence of change. These findings suggest otherwise: The happiness we get from giving appears to sustain itself.

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.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.068
GPT teacher head0.444
Teacher spread0.376 · 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