Finally, someone who “gets” me! Multiracial people value others’ accuracy about their race.
Bibliographic record
Abstract
Monoracial people typically encounter correct views about their race from others. Multiracial people, however, encounter different views about their race depending on the situation. As a result, multiracial (but not monoracial) people may regard race as a less visible aspect of the self that they hope others will verify during social interactions. Multiracial people should therefore value others' accuracy about their race more than monoracial people. In Study 1, multiracial and monoracial participants expected to meet a partner who was accurate or confused about their racial backgrounds. Multiracial (but not monoracial) participants reported heightened interest in interacting with an accurate partner. In Study 2, multiracial (but not monoracial) participants perceived accurate partners as more likely than confused partners to fulfill their needs for self-verification during an interaction. Increased expectations for self-verification, moreover, explained multiracial (but not monoracial) participants' heightened interest in interacting with accurate partners. The results suggest that multiracial (but not monoracial) people view race as an aspect of the self (like personality traits or values) requiring verification from others during interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".