Area‐Based Socioeconomic Characteristics of Industries at High Risk for Violence in the Workplace
Bibliographic record
Abstract
This study examined socioeconomic factors associated with the presence of workplaces belonging to industries reported to be at high risk for worker homicide. The proportion of 2004 North Carolina workplaces in high-risk industries was computed following spatial linkage of individual workplaces to 2000 United States Census Block Groups (n = 3,925). Thirty census-derived socioeconomic variables (selected a priori as potentially predictive of violence) were summarized using exploratory factor analysis into poverty/deprivation, human/economic capital, and transience/instability. Multinomial logistic regression models indicate associations between higher proportion of workplaces belonging to high-risk industries and Block Groups with more poverty/deprivation or transience/instability and less human/economic capital. The relationship between human/economic capital and Block Groups proportion of high-risk industry workplaces was modified by levels of transience/instability. Community characteristics therefore contribute to the potential for workplace violence, and future research should continue to understand the relationship between social context and workplace violence risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".