Bibliographic record
Abstract
“It is almost impossible to progress through a career untouched by incivility,” the author writes. Over the past 20 years she has polled thousands of workers: 98% have experienced uncivil behavior, and 99% have witnessed it. In 2011 half said they were treated rudely at least once a week—up from a quarter in 1998. Rude behavior ranged from outright nastiness and undermining to ignoring people’s opinions to checking e‑mail during meetings. Observing or experiencing rude behavior impairs short ‑ term memory and thus cognitive ability, and has been shown to damage the immune system, put a strain on families, and produce other deleterious effects. Porath has identified some tactics to minimize the effects of rudeness on performance and health. The most effective remedy, she says, is to work holistically on your well ‑ being, rather than trying to change the perpetrator or the relationship. She suggests a two ‑ pronged approach: Take steps to thrive cognitively, which includes growth, momentum, and continual learning; and take steps to thrive affectively, which means experiencing passion, excitement, and vitality at work. INSET: If You Choose Confrontation
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".