A Self-Determination Theory Perspective on Stigma and Prejudice
Bibliographic record
Abstract
Abstract A large body of research documents the negative impact of being a member of a stigmatized group on well-being. Despite these population-level findings, research suggests that there is considerable variation in the well-being of members of stigmatized groups such that although some individuals may be suffering, others may be flourishing. This chapter uses self-determination theory (SDT) as a framework for discussing key determinants of this variation. Stigma represents a social condition of prejudice that can directly thwart people’s basic psychological needs for autonomy and relatedness. The chapter focuses on the role of basic psychological need support and thwarting, particularly in relation to autonomy, in understanding the internalization of negative attitudes toward the self and others; the impact of stigma on well-being through intrapersonal, interpersonal, and institutional-level processes; and the regulation and change of prejudicial attitudes and discrimination. Interpersonal and institutional supports for autonomy and relatedness can reduce the extent to which stigmatizing experiences occur, as well as the harms that follow. Insofar as our modern world has become increasingly global and multicultural, these issues of stigma, prejudice, and inclusion have become correspondingly more salient. This thus represents an important area for continued research and intervention development to which SDT has much to offer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".