E Pluribus Whom? The Limitations of American Identity in Reducing Racial Conflict
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract When diversification becomes salient, a sizable share of white Americans experiences status threat and reacts with backlash. In this paper, we argue that status threat arises because white Americans tend to perceive racial minorities as competing outgroups, not as fellow Americans. Building on recent research suggesting that shared American identity primes can reduce partisan conflict, we test whether reminders of a shared American identity may reduce status threat and thus mitigate subsequent backlash. Across four experiments (total N = 4,062), we replicate status threat as the key mechanism between diversification salience and backlash. Despite various American identity primes and accounting for confounding variables, however, we find little indication that a shared American identity could reduce racial (and, in exploratory analyses, partisan) conflict in America. We discuss the implications for future research and the practical use of a shared American identity when little remains that is shared.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it