“Judge Lynch” in the Court of Public Opinion: Publicity and the De-legitimation of Lynching
Bibliographic record
Abstract
How does violence become publicly unacceptable? I address this question in the context of lynching in the United States. Between 1880 and the 1930s, public discourse about lynching moved from open or tacit endorsement to widespread condemnation. I argue this occurred because of increasing publicity for lynchings. While locals justified nearby lynchings, publicity exposed lynching to distant, un-supportive audiences and allowed African Americans to safely articulate counternarratives and condemnations. I test this argument using data on lynchings, rail networks, and newspaper coverage of lynchings in millions of issues across thousands of newspapers. I find that lynchings in counties with greater access to publicity (via rail networks) saw more and geographically dispersed coverage, that distant coverage was more critical, and that increased risk of media exposure may have reduced the incidence of lynching. I discuss how publicity could be a mechanism for strengthening or weakening justifications of violence in other contexts.
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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 | 0.018 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".