Does a Common Ingroup Identity Reduce Weight Bias? Only When Weight Discrimination Is Salient
Bibliographic record
Abstract
Compared to many other forms of social bias, weight bias is pervasive, socially accepted, and difficult to attenuate. According to the common ingroup identity model, strategies that expand group inclusiveness may promote more positive intergroup attitudes and behaviors, particularly when people are aware of unjust treatment of others included within their shared identity. Considering that most people are not aware of the social justice issue of weight discrimination, we hypothesized that a common ingroup identity would be effective in reducing weight bias primarily when unfair weight-based treatment was made salient (i.e., that fat people experience discrimination in employment). Participants were randomly assigned to conditions following a 3 (discrimination salience: weight discrimination, height discrimination, control) × 2 (group identity: common ingroup, control) design and completed an evaluative measure of weight bias. Results revealed a significant interaction, showing that when weight discrimination was salient, participants in the common ingroup identity condition reported less weight bias than participants in the group identity control condition. When a common ingroup identity was emphasized, weight bias was lower when weight discrimination was salient compared to when height discrimination was salient and the control condition in which nothing about discrimination was mentioned. These results were not moderated by participant weight. This study demonstrates that a common ingroup identity can be effective in reducing weight bias if a cue is provided that fat people experience disparate and unjust outcomes in employment. Given the serious consequences of weight bias for health and well-being, and the relative ease of implementing this prejudice-reduction intervention, the common ingroup identity model has potential application for reducing weight bias in a range of real-world settings. However, these findings should be considered preliminary until they are replicated in well-powered and pre-registered future research.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".