Managing Emotions in the Face of Discrimination: The Differential Effects of Self-Immersion, Self-Distanced Reappraisal, and Positive Reappraisal
Bibliographic record
Abstract
Contending with sexism is associated with negative affective outcomes, including increased anger, anxiety, and depression. Research largely outside of the discrimination domain suggests that emotion regulation strategies, such as reappraisal, can help people manage their emotions after stressful events, attenuating the associated negative affect. Perhaps, these emotion regulation strategies may also be effective in the face of discrimination experiences. The present research examines whether self-distanced reappraisal (Studies 1a & 1b) and positive reappraisal (Study 2) when contending with sexism yield more positive and less negative affective outcomes relative to self-immersion. Contrary to previous research, we find limited support for self- distanced reappraisal as an adaptive emotion regulation strategy for women contending with sexism. Results revealed, however, that positive reappraisal, compared to either self-immersion or self-distanced reappraisal, may be a promising emotion regulation strategy that reduces the affective consequences of sexism. We discuss the implications of these findings for understanding the efficacy of different emotion regulation strategies in the context of discrimination.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".