Protest dynamics understood through the lens of the Civil Rights movement
Bibliographic record
Abstract
This thesis aims to expand on Omar Wasow’s (2020) paper regarding “Agenda Seeding: How 1960s Black Protests Moved Elites, Public Opinion and Voting.” This paper looks into the civil rights movement and its effect on public opinion through Democratic vote share during the 1964, 1968, and 1972 United States presidential elections. Social movement literature rarely justifies the prioritization protest count over protest size for analyses (Biggs 2018). However, newer research suggests that the outcome variable is pivotal to making the right methodological decision (Shuman et al. 2022). Protest count is the most frequent measure used in the cases of policy-related outcomes. I assess protest size to be a stronger measure of attitudinal changes seen in voting behaviour. As literature on the difference in outcomes between nonviolent and violent tactics grows, little has been done to push forward our understanding of the dimensions of violence (McAdam and Su 2002). This thesis sheds light on how more severe forms of violence such as death have a higher predicted effect on negatively affecting public opinion compared to violence at the property level. Finally, to better understand protest dynamics, I include the role of police action. I show how arrests push voting behaviours towards favouring “law and order,” but the use of physical force by law enforcement against nonviolent protesters leads to a “backfire” effect on the state, increasing movement support and shifting public opinion in favour of “civil rights.”
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".