"We Have Had It Up To Here": Murder, Civil Disorder, and Civil Rights in a Western Pennsylvanian Industrial Town
Bibliographic record
Abstract
In 1970, citizens of New Castle, Pennsylvania, a small industrial city an hour north of Pittsburgh, responded to the racially motivated murder of a local black Vietnam veteran in that city with vandalism and firebombing that forced the mayor to place the city under a state of emergency for three days. The series of exchanges preceding and following the murder reveals much about that city's history, and how several factors influenced local forms of racism. Existing scholarship has focused on racialized policies and practices in two spatial extremes—large cities and small towns—while this analysis seeks to illustrate how local, regional, and national influences shaped what forms of race-based policies and practices in spaces between these municipal extremes were permissible. Beyond place and space, this research contributes to a different set of conversations about the ways identity and community are articulated through the actions of individuals and groups, and how those understandings are shaped by individual and collective memory. This analysis begins by situating Ronald Mitchell's murder within the historical context of 1970s New Castle, broadens to place New Castle amid much larger and smaller municipalities across the country, and briefly contours some historical forces that shaped racism in policy or practice across time. I illustrate how federal, state, and local authorities responded to crises comparable to that which occurred in response to Mitchell's murder in the 1960s, and highlight how the underlying causes identified during investigations by those bodies manifested throughout the city's history and at the scene of Mitchell's murder. I also explore the role of institutions and memory in shaping knowledge and use of the past and build upon earlier scholarship in asserting their centrality to equitable futures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".