What it Means to be American: Identity Inclusiveness/Exclusiveness and Support for Policies About Muslims among U.S.‐born Whites
Bibliographic record
Abstract
Abstract Americans’ support for policies targeting Muslims was hotly debated during the 2016 presidential campaign. This study of U.S.‐born White Americans seeks to move beyond explanations of this political polarization as a matter of liberal versus conservative, Democrat versus Republicans by focusing on the content of the superordinate American identity, in terms of how inclusive versus exclusive it is. In line with the ingroup projection model, we expected that a more inclusive representation of the American identity would be related to support for more welcoming (rather than hostile) policies about Muslim people. White Americans (N = 237) were recruited online during the 2016 U.S. presidential campaign (June 2016). Results supported our hypothesis and showed the independent associations of identity inclusiveness and exclusiveness with policy support. This study makes three important contributions to a growing literature on the relation between national identity representations and hostility toward immigrants and minorities: (1) directly and independently measuring inclusive and exclusive representations of the superordinate identity, alongside national identity, party affiliation, and political ideology; (2) focusing on Muslims, an understudied group targeted by a great deal of divisive political rhetoric in the 2016 campaign; and (3) considering policy support rather than general attitudes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".