Whose family fits? Categorization and evaluation of same-sex and cross-race-parent families
Bibliographic record
Abstract
As family structures diversify, attitudes towards “nontraditional” families (e.g., same-sex-parent and cross-race families) appear to be becoming more favorable. Despite more favorable attitudes, we propose that explicitly and implicitly people view nontraditional families as less family-like than traditional (i.e., heterosexual, same-race) families. We also propose that people will hold the behavior of nontraditional (vs. traditional) families to higher standards. In Study 1, participants explicitly rated nontraditional (vs. traditional) family photos as less family-like and as less loving. In Study 2, using a reaction-time measure, participants took longer to correctly categorize nontraditional (vs. traditional) families into the family category, suggesting that at an implicit level people have greater difficulty recognizing nontraditional families as “family.” In Studies 3 and 4, ambiguous (i.e., positive and negative) behavior licensed more harsh evaluations of a nontraditional family—but did not affect evaluations of a traditional family—relative to learning only positive family behavior. Despite survey data that suggest that people’s views of nontraditional families are becoming more favorable, our evidence indicates that people nonetheless harbor prejudice against certain family structures. Beyond documenting two biases against nontraditional families, this work highlights the need for prejudice researchers to examine meaningful levels of social identity, such as family units, that are intermediate between individuals and broad social classes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".