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Record W2620016909 · doi:10.1177/0956797617701186

Dying Is Unexpectedly Positive

2017· article· en· W2620016909 on OpenAlexaff
Amelia Goranson, Ryan S. Ritter, Adam Waytz, Michael I. Norton, Kurt Gray

Bibliographic record

VenuePsychological Science · 2017
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsKellogg's (Canada)
FundersDivision of Social and Economic SciencesCharles Koch FoundationJohn Templeton FoundationNational Science Foundation
KeywordsPsychologyPerceptionPoetryAmyotrophic lateral sclerosisSocial psychologyMedicineDiseaseLiteratureInternal medicineArt

Abstract

fetched live from OpenAlex

In people's imagination, dying seems dreadful; however, these perceptions may not reflect reality. In two studies, we compared the affective experience of people facing imminent death with that of people imagining imminent death. Study 1 revealed that blog posts of near-death patients with cancer and amyotrophic lateral sclerosis were more positive and less negative than the simulated blog posts of nonpatients-and also that the patients' blog posts became more positive as death neared. Study 2 revealed that the last words of death-row inmates were more positive and less negative than the simulated last words of noninmates-and also that these last words were less negative than poetry written by death-row inmates. Together, these results suggest that the experience of dying-even because of terminal illness or execution-may be more pleasant than one imagines.

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)0.004
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.063
GPT teacher head0.445
Teacher spread0.382 · 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

Citations49
Published2017
Admission routes1
Has abstractyes

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