The inauguration of David W. Leebron: the beginning of his presidency of Rice University 2006
Bibliographic record
Abstract
s academic career in legal education before coming to Rice University seemed to the search committee perfectly to prepare him for leadership of the university even though it has no law school.The personal qualities he possessed -"an academic scholar of the highest order," as one of his colleagues put it; a person of "unquestioned trustworthiness, integrity, and honesty," said another; and described further as one who consults widely, listens carefully, and then acts determinedly-clearly met the expectations of the search committee.His goals and accomplishments after eight years as dean of the Columbia University School of Law suggested the kinds of things he might be expected to do at Rice.The search committee (and the law school dean) were very aware that a great university was far larger and more complex than a single professional school, but the nature of the changes put underway at Columbia's Law School by Leebron strongly recommended him to the Rice committee.When
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".