Bibliographic record
Abstract
An aggressive, bullying boss I used to work for would build himself up into a state of rage at least three times a week. He once became so enraged that he actually threw the office kettle out of the window shouting, “You’re not drinking my tea and coffee! ” as staff looked on in disbelief. My boss really drove me up the wall to a point where I started developing stomach problems, which my doctor linked to stress. My husband worked for the NHS and became the target of the department bully. When he stood up for himself, the manager stood behind his desk waiving his fists and saying my husband was mentally impaired and had lost all his friends. Employees shared these stories and others on the BBC Web site following a news story on menacing bosses and their role in workplace stress (BBC, 2003). Although these stories are extreme, they help bring to light the vital role leaders play in organizations and the profound impact they have on the stress and well-being of those they lead. 89 Authors ’ Note: Preparation of this chapter was supported by funding from the Nova Scotia Health Research Foundation to the first and third authors and from the Social Sciences and Humanities Research Council of Canada to the first and fourth authors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.093 | 0.013 |
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".