Race is Still Black and White: Voluntary Racial Phenotypic Change Elicits Meaning Threat and Backlash
Bibliographic record
Abstract
We offer evidence that a target who voluntarily changes his/her racial phenotypic features causes perceivers to engage in two-pronged social policing of racial group boundaries: (a) vilifying and disliking the target (cognitive and affective backlash; external policing) (Experiments 1a-1b, 2, & 3) and (b) increasing own racial essentialism, in response to a meaning threat (internal policing) (Experiment 3). In all experiments, participants received a vignette of a protagonist that underwent non-elective surgery (white/Asian, Experiments 1a-1b; white/Black, Experiments 2-3). In the voluntary change condition, the protagonist asks that the surgeon change his/her racial features to resemble that of a different race whereas, in the involuntary change condition the protagonist asks that the surgeon keep his/her racial features intact (Experiment 1: eye shape, Experiment 2: Afrocentric features). Findings supported the predictions and showed a dissociation between similarity and categorization judgments, underscoring the essentialized versus socially constructed nature of beliefs about race.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".