Bibliographic record
Abstract
Purpose The purpose of this paper is to document the racist undertones of Donald Trump’s Presidential campaign rhetoric and draw implications regarding its impact on equality, diversity, and inclusion. Most contemporary individuals reject explicitly racist beliefs and strive to present themselves as having egalitarian attitudes toward other races and ethnicities. However, commonly held implicit biases toward historically marginalized racioethnic groups drive negative effect that is often unconscious and unacknowledged. Inconsistency between the conscious and unconscious aspects of contemporary racism generates a population of individuals who are uncomfortable with their attitudes, creating an opening for politicians willing to leverage racist rhetoric and gain support by resolving this inconsistency. Design/methodology/approach This paper applies social psychological theory and research to address the questions of what attracts otherwise non-racist individuals to racist-tinged rhetoric. The paper also provides theory-based interventions for reducing the attractiveness and impact of racist political campaigns. Findings Supporters of racist politicians resolve the conflict between their negative feelings toward racioethnic minorities and their espoused anti-racist views by distancing themselves from racist rhetorical content in three ways: by denying that racist statements or actions occurred, denying that the statements or actions are racist, and/or by denying responsibility for racism and its effects. These techniques provide supporters with validation from an authority that they can express their negative affect toward out-groups and still consider themselves to be good people and not racists. Practical implications Distancing from racism has allowed contemporary American extremists to reframe themselves as victims of closed-minded progressives seeking to elevate undeserving and/or dangerous out-groups at the in-group’s expense. Effective anti-racism techniques are needed to counter implicit biases in order to limit the attractiveness of extremist views. Implicit biases can be effectively reduced through training in counter-stereotypic imaging, stereotype replacement, and structured inter-group interaction. Effectively countering denial of the facts involves affirming the audience’s belief system while building skepticism toward the sources of misinformation. Social implications While countering racist politicians requires commitment, these efforts are essential for protecting the identity of the USA as a society striving toward equality, diversity, and inclusion. Originality/value By articulating the social psychological principles underpinning racist-tinged populist rhetoric, this paper explains the attractiveness of racist statements by politicians, which tends to be under-estimated.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".