Bibliographic record
Abstract
Abstract The more that devalued group members experience stigmatization, the worse their physical and mental health, well-being, and performance will be. However, the effects of stigmatization are often mixed, weak, and conditional. We should expect such variability in how devalued group members respond to stigmatization because resilience in the face of challenges is possible, depending on how stressful stigmatization is for people. Using the transactional model of stress (Lazarus and Folkman, 1984) as an organizing framework, I provide evidence that people will have different reactions to stigmatization depending onprimary appraisals—that is, how harmful and self-relevant they appraise it to be—and onsecondary appraisals—that is, whether or not they believe that they have the resources to cope with it. My review of the literature suggests that a stronger ingroup identification, stronger identification with a negatively stereotyped domain, chronic beliefs about stigmatization, and beliefs about meritocracy create vulnerabilities to stigmatization because they lead people to appraise stigmatization as more harmful and self-relevant. Furthermore, psychological optimism, a sense of control, self-esteem, as well as high socioeconomic status, a stronger identification with one's ingroup, and positive evaluations of the ingroup create resilience to discrimination because they allow people to perceive themselves as having the resources needed to cope with stigmatization. In conclusion, people will respond to the same potential stressor in different ways, depending on how self-relevant and harmful they perceive it to be and whether or not they perceive themselves as having the resources to cope. Thus, attention should be directed to developing families, communities, institutions, and societies that can provide people with the resources that they need to be resilient.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".