Bibliographic record
Abstract
Abstract It is widely accepted that political discourse in recent years has become more openly racist and more filled with wildly implausible conspiracy theories. Dogwhistles and Figleaves explores certain ways in which such changes—both of which defied previously settled norms of political speech—have been brought about. Jennifer Saul shows that two linguistic devices, dogwhistles and figleaves, have played a crucial role. Some dogwhistles (such as “88,” used by Nazis online to mean “Heil Hitler”) serve to disguise messages that would otherwise be rejected as unacceptable, allowing them to be transmitted surreptitiously. Other dogwhistles (like the 1988 “Willie Horton” ad) work by influencing people in ways that they are not aware of, and which they would likely reject were they aware. Figleaves (such as “just asking questions”) take messages that could easily be recognized as unacceptable, and provide just enough cover that people become more willing to accept them. Importantly, these work against the background of a divided public. They are particularly effective in influencing people who are conflicted yet malleable—those who don’t want to be racist, for example, but are willing to be convinced that something which seems racist really isn’t. Saul shows how these dogwhistles and figleaves have both exploited and widened existing divisions in society, and normalized racist and conspiracist speech.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".