Discourse in the Segregated City: Racial Violence, Capital, and Milwaukee's Media
Bibliographic record
Abstract
This paper examines the news reportage surrounding two race-related incidents that occurred in 2011 in Milwaukee, WI, one of America’s most segregated cities. The altercations involved Black youths violently encountering white attendees of a park in one case, and white attendees of the Wisconsin State Fair in the other. Milwaukee Journal Sentinel stories failed to consider longstanding social and economic injustices that might give rise to such behavior, adopting instead a relatively colorblind position. This strategy fosters the illusion that the conditions between races are equal, despite the unequal, discriminatory effect capital and the state have had on African Americans. While online Journal Sentinel reportage contained a subdued racism, many online reader comments appearing below articles were openly white supremacist. Editorials, in accordance with the coverage, called for more responsible parents instead of a more thoughtful capitalism, i.e., one that would not export manufacturing jobs or deliberately phase out the need for Black labor. Drawing on scholars such as Stuart Hall et al., I explain from a Marxian standpoint why Milwaukee’s coverage and reader comments take the shape they do. I further argue that these circumscribed case studies can help us understand the ideology at work behind more prominent (and tragic) racial incidents, such as the 2014 summer protests in Ferguson, MO.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".