Bibliographic record
Abstract
Today the threat of violence impacting worker safety and business operations is a major concern. It is crucial that thoughtful violence prevention policies and supporting violence response plans be developed before any incidents occur in order to properly prepare to use, respond, engage, and react appropriately. Once violence begins or ends is not good enough. The threats are real, and the risks must be managed. A violent threat from a current or former employee, domestic violence or relationship violence spillovers, and the threat posed by criminals committing crimes against people and property are concerns for which all organizations must prepare. Incident avoidance is not acceptable – indeed, most likely not possible. Our job is to make it manageable.This book, Combating Workplace Violence, provides a basic understanding of workplace violence as well as prevention policy and plan development in nontechnical terms. The key to the successful development and implementation of a w+orkplace prevention policy is the collaborative proactive leadership of company executives and management and the assistance of a qualified, reputable consultant. While the information and tools contained here are designed to serve as a baseline for any organization’s solution to workplace violence, the material is useful to inform and educate any member of an organization. The unique framework (V-REEL®) for analyzing the organization’s internal environment to determine what can be done to try to eradicate or reduce workplace violence is especially useful. Ancillaries following each chapter provide additional information and tools to assist your planning. We envision this book being used to inform managers, human resource professionals, workers, and academics in all types of organizations. Hopefully, using the material and framework of this book, more organizations will develop policies, procedures, and practices to prevent workplace violence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".