When Self-Compassion Loses its Luster: Ratings of Self-Compassionate and Self-Critical Responding among Passionate Students
Bibliographic record
Abstract
People generally recognize the benefits of responding to failures with self-compassion (i.e., with self-kindness, a sense of common humanity, and mindfulness) rather than self-criticism. In this research, we replicated this effect with passionate students who had all reported that academics was a pursuit that they enjoyed and was important to them, and tested if it was influenced by levels of harmonious passion (a balanced and flexible engagement in academics), and obsessive passion (a rigid and uncontrollable desire to pursue academics). Passionate students (n = 241) reported how they would evaluate themselves (e.g., confident) and the anticipated outcomes that they expected to occur (e.g., goal achievement) if they were to respond to academic difficulties with either self-compassion or self-criticism. Results showed that many of the advantages that students believed self-compassion had over self-criticism, such as enhanced future performance and feelings of confidence and success, disappeared when academic passion involved high levels of obsessive passion and low levels of harmonious passion (i.e., pure obsessive passion). To promote self-compassion in academics, these findings suggest that the benefits of self-compassion need to be emphasized for students with pure obsessive passion.
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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 | 0.002 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".