Bibliographic record
Abstract
More than 100 years after Upton Sinclair denounced the massive slaughterhouse complex in Chicago as a “jungle,” qualitative case study research has documented numerous negative effects of slaughterhouses on workers and communities. Of the social problems observed in these communities, the increases in crime have been particularly dramatic. These increases have been theorized as being linked to the demographic characteristics of the workers, social disorganization in the communities, and increased unemployment rates. But these explanations have not been empirically tested, and no research has addressed the possibility of a link between the increased crime rates and the violent work that takes place in the meatpacking industry. This study uses panel analysis of 1994-2002 data on nonmetropolitan counties in states with “rightto-work” laws (a total of 581 counties) to analyze the effect of slaughterhouses on the surrounding communities using both ordinary least squares and negative binomial regression. The findings indicate that slaughterhouse employment increases total arrest rates, arrests for violent crimes, arrests for rape, and arrests for other sex offenses in comparison with other industries. This suggests the existence of a “Sinclair effect” unique to the violent workplace of the slaughterhouse, a factor that has not previously been examined in the sociology of violence.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".