“They Might Be Wondering Why I Didn’t Set My Sights Higher”: Associative Stigma in Sexual and Romantic Relationships with Fat Partners
Bibliographic record
Abstract
Fatness and fat people are pervasively stigmatized in Western cultures, with significant negative implications for fat people's well-being. Negative evaluations of those in sexual and romantic relationships with fat people (i.e. associative stigma) may have harmful implications for shared relational well-being. Here, we examined whether non-fat (i.e. thin) sexual and romantic relationship partners of fat people experience associative stigma. First, we conducted a mixed-methods study with thin partners of fat people to elucidate their experiences of associative stigmatization and impacts on relational and sexual well-being. Many participants reported experiencing associative stigma, which, in tandem with relationship stigma, predicted lower relationship satisfaction but not sexual satisfaction. The most commonly reported experiences of associative stigma included others' assumptions that the fat partner is inferior, weight-based microaggressions, and negative attention in public. In a second, experimental study, we randomly assigned a second sample of participants to read one of 16 vignettes about mixed-weight (one fat and one thin partner; experimental condition) or same-weight (both thin; control) couples. Stimulus couples varied by target (thin partner) gender (male vs. female), relationship orientation (same-gender vs. other-gender), and relationship type (sexual vs. romantic). We found mixed support for our hypotheses that thin partners of fat people, relative to thin people in same-weight relationships, would be stigmatized. We conclude by calling for greater attention to the potential for associative stigma to influence sexual and romantic relationship outcomes.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".