Bibliographic record
Abstract
Editor—Clark and Smith's focus on internal and external funding and leadership is paramount in improving the state of academic medicine.1 Academic health care has deteriorated because academicians are poorly remunerated (compared with their peers) for their academic work, unless they partner with industry. Unfortunately, industry's raison d'etre seems to be the promotion of therapeutics or diagnostics rather than of education for education's sake. Thus all education partnered with industry is potentially tainted by the underlying profit motive. Additionally, research productivity is important for much of the academic advancement in faculties of medicine and health science. Because there has been a disinvestment from clinical research by government organisations, researchers who wish to proceed through the ranks via research must increasingly rely on industry funds to support their work. Again, all such work is potentially tainted by the profit motive. Yet the researchers who are able to forge the ties with industry are the educational leaders. Although all faculties of medicine and health science have expert educators and teachers, they often remain small cogs in a larger machine. They deliver well intentioned (and often important) research, curriculums, and teaching encounters but are usually overshadowed by the more powerful and better funded researchers who lead. These leading researchers speak to (and influence) medical students, postgraduate trainees, and practising clinicians. They ascend the academic ranks and make important policy decisions for divisions, departments, faculties, and the community. They are academic medicine. For academic medicine to revitalise it needs leadership and money. However, the money must be both substantial and independent of industry directives. Similarly, the leadership must consist of that extremely and increasingly rare breed: a visionary who sees research, education, and clinical teaching as equally important, who has been successful at some or all of these, and who has managed to be so without the strong ties that bind many of us to industry.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.026 | 0.042 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".