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Record W4414265852 · doi:10.1080/15298868.2025.2554968

How self-compassion informs decision-making in ordinary times

2025· article· en· W4414265852 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSelf and Identity · 2025
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAction (physics)Affect (linguistics)Identity (music)NarrativeEmpathy

Abstract

fetched live from OpenAlex

Do people with a life history of responding adaptively to personal losses worry less about potential losses in the future? The present research tested the hypothesis that individuals higher in self-compassion would value potential losses less during decision-making. In Study 1, crowdsourced participants (N = 305) in an online survey completed measures of their preoccupation with avoiding losses and answered investment scenarios with escalating loss-potential. Those higher in self-compassion reported lower assessment vs. locomotion modes of self-regulation, prevention vs. promotion regulatory focus, and fear of invalidity. They also invested larger amounts in the scenario with the highest loss-potential and took more “double-or-nothing” chances for gain. In Study 2, undergraduate participants (N = 205) in an in-lab experiment showed similar trait-correlations as in Study 1. Those higher in self-compassion took greater chances of misremembering items in a game with high-penalty vs. low-penalty instructions. The results link self-compassion with ordinary cost/benefit decision-making and may, therefore, have implications for the development of self-control.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.332
Teacher spread0.322 · 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