Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it