Bibliographic record
Abstract
Leadership is a quality that can be learned. It is a behavior that one practices, and, after lots of practice, it becomes a habit. This is a lesson I learned from my father, who was a career pilot in the US Air Force and instilled this into me and my siblings from a very early age. It is also something I have learned in observing others. I have frequently asked why some people from certain disciplinary backgrounds seem to have an advantage in the leadership area. Think of the backgrounds of our Presidents, for example; so many of them have been attorneys. Members of Congress, as well, also frequently come from that disciplinary background. Key decision makers in government frequently come from economics backgrounds. I have also asked why this is the case. Frequently, the answer seems to be that these disciplines define themselves as being those that create leaders, not that they limit their members' aspirations. Why are so few veterinarians in leadership positions? It seems quite a paradox that they are not. The assets of an education in veterinary medicine are many. The education provides a very broad background in systems biology, medicine, and public health. There are many career paths for veterinarians. Most choose private practice, but, beyond that, career paths exist in industry, particularly the biomedical industry; in trade associations; in government and industry research; and in public health and regulatory positions. There are also many opportunities in academia, certainly in colleges of veterinary medicine but, beyond that, also in human medicine and in the biology disciplines. International opportunities also exist in governmental and non-governmental organizations, such as the Food and Agriculture Organization at the United Nations and the World Health Organization, and in advocacy and lobbying. Veterinarians are also making news these days. The emerging zoonotic diseases that have seized headlines in papers around the world give prominence to veterinarians and the skills they bring to bear in fighting current outbreaks and preventing future outbreaks of these diseases, such as SARS, Ebola, West Nile virus, and even HIV/AIDS.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".