Bibliographic record
Abstract
Best known for his Nobel prize-winning work on derivatives pricing, Myron Scholes made significant contributions to a wide range of topics in financial economics, from asset pricing to dividend policy and tax incentives. Born 1 July 1941 in Timmins, Ontario, Canada, he earned a Bachelor’s degree in Economics from McMaster University in Hamilton, Ontario in 1962. He then entered the MBA course at the University of Chicago, transferring to the Ph.D. course after his second year. He earned his MBA in 1964 and his Ph.D. in 1968, writing his dissertation on the effects of information and signalling on the shape of the demand curves for traded securities. Upon finishing graduate studies he took a position as Assistant Professor of Finance at the Sloan School of Management at the Massachusetts Institute of Technology. After five years he moved to the Graduate School of Business at the University of Chicago. He was first Visiting Professor, and then in 1974 he accepted a permanent position. He stayed there until 1981, when he became Visiting Professor at Stanford University for two years, becoming a permanent faculty member of the university’s Business School and Law Schools in 1983, remaining there until his retirement in 1996.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".