The effects of stereotype threat on the eating behaviours and intellectual performance of overweight and obese women
Bibliographic record
Abstract
Stereotype threat is the experience of apprehension that individuals feel in situations where their behaviour may be seen as evidence confirming a negative stereotype about their group. This threat leads to behaviour consistent with the stereotype. In contrast, unthreatened individuals perform equivalently to members of an unstereotyped group. Overweight and obese individuals are targets of many stereotypes, including the stereotypes that they lack control of their eating, and that they are less intelligent than are normal-weight individuals. Therefore, the purpose of these studies was to investigate the effects of stereotype threat on the eating behaviours and intellectual performance of overweight and obese women. It was hypothesized that overweight and obese females exposed to a stereotype threat would eat significantly more and would perform more poorly on an intellectual measure than would overweight and obese females unexposed to stereotype threat, and normal-weight participants in either condition. The performance of the latter three groups was not expected to differ. Domain identification was included as a moderator, and it was predicted that individuals highly invested in the targeted domain would be most reactive to the threat. In both Studies 1 and 2, stereotype threat was introduced with a vignette detailing discrimination against obese individuals, after which the behaviour of interest (eating in Study 1, intellectual performance in Study 2) was measured. Moreover, in both studies, weight was defined both objectively (body mass index) and subjectively (participants' self-classification). In Study 1, both the objective and the subjective weight analyses revealed that overweight participants ate more in the threat than in the control condition. Moreover, the meaning of this difference was clarified in the subjective weight analyses: overweight participants appeared to restrict their eating in the control condition, so that the disinhibitory effect of stereotype threat simply increased their consumption to the amounts eaten by their normal-weight counterparts, whose eating was unaffected by the experimental manipulation. Study 2 did not find any evidence of stereotype-consistent behaviour (i.e., impaired intellectual performance) in overweight and obese participants following a stereotype threat.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".