The Cost of Being “True to Yourself” for Mixed Selves: Frame Switching Leads to Perceived Inauthenticity and Downstream Social Consequences for Biculturals
Bibliographic record
Abstract
A growing population of biculturals—who identify with at least two cultures—often frame switch, adapting their behavior to their shifting cultural contexts. We demonstrate that frame switching biculturals are perceived as inauthentic by majority Americans and consequently seen as less likable, trustworthy, warm, and competent compared to biculturals who do not frame switch or a neutral control (Studies 1–3, N = 763). In Study 2, describing the bicultural’s behavior as authentic despite its inconsistency partly alleviated the negative effects of frame switching. In our preregistered Study 3, majority American women were less romantically interested in and less willing to date a bicultural who frame switched in his dating profiles (mediated by inauthenticity). The way biculturals negotiate their cultures can have social costs and create a barrier to intercultural relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".