From mid-career professor to chairperson: What remains similar what is different?
Bibliographic record
Abstract
For a Mid-career Professor in Germany, there are defined clinical and teaching responsibilities. One can focus either on one’s research or on clinical work and teaching. When tasks are becoming more demanding or significant overarching decisions need to be taken, there is always a chairperson to be asked or to help delegate tasks. As chairperson, one is mostly independent from other persons except for the dean of the medical faculty. One is however, at least in Germany, the chairpersons fully responsible for keeping up teaching, patient care, research as well as running the department. The Chairperson is measured by the achievements of these four tasks. It need special attention to keep up a balanced time schedule to cover clinical care, research, teaching and departmental management. A good chairs means working together with your staff on long-term goals, developing the department fruitfully and trying to fulfil these goals. Disclosure No significant relationships.
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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 | 0.024 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".