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Record W3017828742 · doi:10.1093/jcr/ucaa020

A Little Piece of Me: When Mortality Reminders Lead to Giving to Others

2020· article· en· W3017828742 on OpenAlexaff
Lea Dunn, Katherine White, Darren W. Dahl

Bibliographic record

VenueJournal of Consumer Research · 2020
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsBC Innovation CouncilUniversity of British Columbia
Fundersnot available
KeywordsMaterialismTranscendence (philosophy)DonationPossession (linguistics)Salience (neuroscience)Social psychologyMortality saliencePsychologySelf-transcendenceProduct (mathematics)EpistemologyCognitive psychologyPhilosophyLawPolitical scienceMathematics

Abstract

fetched live from OpenAlex

Abstract Past research demonstrates that reminders of one’s own mortality can lead to materialistic and self-serving consumer behaviors. In contrast, across five studies, we explore a condition under which mortality salience (MS) leads to increased tendency to give away one’s possessions—when the donation act is high in transcendence potential. We propose and find that consumers are more likely to donate their possessions to charity under MS (vs. comparison conditions) when the product is considered highly (vs. not highly) connected to the self. Moreover, we demonstrate that this tendency manifests only when transcendence is attainable through donation. In support of the proposition of transcendence as the underlying mechanism, the observed effects are attenuated under conditions where: (1) transcendence has already been satiated via alternative means or (2) the donated possession will not transcend the self (i.e., its physical integrity is lost by being broken down and recycled). The theoretical and practical implications of the work are discussed.

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)0.001
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.247
GPT teacher head0.485
Teacher spread0.238 · 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

Citations64
Published2020
Admission routes1
Has abstractyes

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