Multiple Stereotypes of Stepfathers
Bibliographic record
Abstract
Three separate studies were conducted to determine whether university students hold multiple stereotypes of stepfathers. In the first study, 116 undergraduate students were asked to list all of the traits that are typically associated with stepfathers. After semantically similar traits were grouped together, 77 distinct traits (45 negative and 32 positive) remained. In the second study, 34 undergraduate students were asked to sort the 77 traits from Study 1 into one or more groups; each group consisting of traits that could be found together in one and the same stepfather. Hierarchical cluster analysis resulted in nine negative and six positive trait clusters, indicating that people have multiple stereotypes for stepfathers. In the final study, 29 undergraduate students were asked to rate their impressions of a person who possessed each of the 15 sets of traits, using a semantic differential scale. In addition, students were asked to indicate how typical each trait cluster (or stereotype) was of stepfathers in general. The results indicated that the positive stereotypes received higher ratings than the negative stereotypes. Further, the positive stereotypes were seen as more typical of stepfathers, while the negative stereotypes were not viewed as typical of stepfathers.
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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 | 0.000 |
| 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 | 0.000 |
| 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".