Having “been there” doesn’t mean I care: When prior experience reduces compassion for emotional distress.
Bibliographic record
Abstract
The current research found that participants who had previously endured an emotionally distressing event (e.g., bullying) more harshly evaluated another person's failure to endure a similar distressing event compared with participants with no experience enduring the event or those currently enduring the event. These effects emerged for naturally occurring (Studies 1, 3, and 4) and experimentally induced (Study 2) distressing events. This effect was driven by the tendency for those who previously endured the distressing event to view the event as less difficult to overcome (Study 3). Moreover, we demonstrate that the effect is specific to evaluations of perceived failure: Compared with those with no experience, people who previously endured a distressing event made less favorable evaluations of an individual failing to endure the event, but made more favorable evaluations of an individual managing to endure the event (Study 4). Finally, we found that people failed to anticipate this effect of enduring distress, instead believing that individuals who have previously endured emotionally distressing events would most favorably evaluate others' failures to endure (Study 5). Taken together, these findings present a paradox such that, in the face of struggle or defeat, the people we seek for advice or comfort may be the least likely to provide it.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".