Did Secretary Clinton lose to a ‘basket of deplorables’? An examination of Islamophobia, homophobia, sexism and conservative ideology in the 2016 US presidential election
Bibliographic record
Abstract
The current study compared attitudes towards LGBTQ individuals, racism, Islamophobia, ambivalent sexism and conservative ideology across Hillary Clinton voters, Donald Trump voters and third party/undecided voters in the 2016 US presidential election. Participants (n = 249) intending to vote for Clinton had significantly lower scores on all attitude measures compared to Trump and third party/undecided voters, with the exception of Islamophobia, where Clinton and third party/undecided voters had significantly lower scores than Trump voters. A multinomial logistic regression was run to assess age, education, attitudes towards LGBTQ individuals, Islamophobia, sexism and social dominance orientation, as predictors of being a Trump, Clinton or a third party/undecided voter. Attitudes towards LGBTQ individuals, Islamophobia, sexism and social dominance orientation were significant predictors of voting behaviour such that those who were less homophobic, less Islamophobic, less sexist and had less of a social dominance orientation were more likely to vote for Clinton than for Trump or a third party candidate. Ambivalent sexism was the strongest predictor of voting for someone other than Clinton, regardless of whether participants identified as Trump or third party/undecided voters. Results are discussed within the context of understanding the role of multiple prejudices in determining the outcome of the 2016 US presidential election.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".