Bibliographic record
Abstract
We've all heard of (or experienced) the "boss from hell." But that's just one form that incivility in the workplace can take. Rudeness on the job is surprisingly common, and it's on the rise. Whether it involves overt bullying or subtle acts of thoughtlessness, incivility takes a toll. It erodes productivity, chips away at morale, leads employees to quit, and damages customer relationships. Dealing with its aftermath can soak up weeks of managerial attention and time. Over the past 14 years the authors have conducted interviews with and collected data from more than 14,000 people throughout the United States and Canada in order to track the prevalence, types, causes, costs, and cures of incivility at work. They suggest several steps leaders can take to counter rudeness. Managers should start with themselves-monitoring their own behavior, asking for feedback on it, and making sure that their actions are a model for others. When it comes to managing the organization, leaders should hire with civility in mind, teach it on the job, create group norms, reward good behavior, and penalize bad behavior. Lest consistent civility seem an extravagance, the authors caution that just one habitually offensive employee critically positioned in an organization can cost millions in Lost employees, lost customers, and lost productivity.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".