The role of competence and warmth in the evaluation of obese individuals
Bibliographic record
Abstract
This study examined the effect of varying levels of competence and warmth portrayed by an obese woman, on the evaluation of obese individuals in general. The Stereotype Content Model (SCM) provided a theoretical basis for the four interventions and one control group tested in this study. The interventions involved presenting, either online or in-person, a vignette of an obese woman with varying levels (high versus low) of competence and warmth to 400 first year university students. Several measures were used to evaluate feelings, attitudes, stereotypes, and levels of perceived competence and warmth both prior to receiving the intervention and at three follow-up times. Results revealed significant effects of the warmth intervention on measures of competence and warmth at the post-test 1 time, and of the warmth intervention on measures of competence in examining changes over a two-week time period. Results also showed significant effects of presentation condition, the most interesting finding being that those in the online condition reported the greatest decrease in negative stereotypes and evaluations. Limitations of this study including design flaws are identified, as are suggestions for future research, and implications of these findings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".