How self-compassion informs decision-making in ordinary times
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".