Bibliographic record
Abstract
The author presents advice to deans and chairs of academia by imagining what Machiavelli might recommend were he to write a modern version of The Prince for academics. "Machiavelli" cautions that since modern academic "princes" have little power (except, perhaps, over teaching and laboratory space), the success of their rule depends upon respect. Regarding the choice of an academic prince, find someone who can be a good role model, set standards, and reward academic excellence, and who will, above all, be respected. Avoid choosing a prince who is a nice, nonthreatening candidate with "good human relations" and "good executive skills." Choose candidates who are already successful and fulfilled and who will see the new post not as a promotion or a balm for their insecurity, but as an intrusion into their academic lives. Fill empty positions as quickly as possible-better a weak prince than no prince at all. Seek short terms for princes, both because respected academics will want to return to their normal lives as soon as possible, and because with short mandates, greater chances can be taken with young, unproved, but promising candidates. At the same time, the appointment of aging administrators who have lost their academic skills is to be avoided. Above all, respect the throne-i.e., the position of chair or dean-even if the person holding the position may not deserve the respect, since when the prince retires with honor, the position becomes more attractive to future good candidates.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.075 | 0.049 |
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".