Love is Not Colorblind: An Investigation of the Racial Hierarchy of Mate Preferences
Bibliographic record
Abstract
Not my type is the usual invocation when rejecting potential lovers who don’t align with the racial hierarchy of mating preferences. The largely unchallenged norm of interracial intimacy aversion, particularly how the desire for some racial groups and rejection of others reinforces existing racial inequities, is inconsistent with the blanket notion of greater interracial acceptance. Our investigation assessed the openness of monoracial and multiracial individuals to form interracial romantic relationships. We partially replicated an interracial mate preference known as the Multiracial Dividend Effect, finding that most monoracial groups equally preferred same-race lovers and interracially dating multiracials, and they preferred interracially dating someone multiracial over any monoracial group, whereas Multiracials were more open to interracially dating any monoracial group than monoracials were to interracially date each other. In addition, Hispanic-White and East Asian-White multiracials were more open to interracially dating White individuals than their respective monoracial in-group members, and East Asian-White multiracials were more open to interracially dating all monoracial minority groups than monoracial East Asian participants. Finally, half-White multiracials are more likely to be in partial-racial couples (e.g., former President of the United States Barack Obama is Black-White multiracial and the former First Lady of the United States, Michelle Obama, is Black) whereas interminority multiracials are more likely to be in 100% interracial/non-overlapping couples (e.g., Vice President of the United States Kamala Harris is interminority Tamil Indian and Black whereas the Second Gentleman of the United States, Doug Emhoff, is White).
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".