Bibliographic record
Abstract
Discussants: PHILIP G. BERGER, University of Chicago - Graduate School of Business; ROBERT F. BRUNER, University of Virginia - Darden Graduate School of Business; DAVID J. DENIS, Purdue University - Krannert School of Management; STEPHEN R. FOERSTER, University of Western Ontario - Richard Ivey School of Business; ROBERT H. GERTNER, University of Chicago - Graduate School of Business; STUART C. GILSON, Harvard Business School; JOHN R. GRAHAM, Duke University - Fuqua School of Business; LAURIE SIMON HODRICK, Columbia Business School; STEVEN N. KAPLAN, University of Chicago - Graduate School of Business; LARRY H.P. LANG, Chinese University of Hong Kong; VOJISLAV MAKSIMOVIC, University of Maryland - Robert H. Smith School of Business; JOHN D. MARTIN, Baylor University - Hankamer School of Business; JOHN G. MATSUSAKA, University of Southern California - Marshall School of Business; RANDALL MORCK, University of Alberta School of Business; GORDON M. PHILLIPS, University of Maryland - Robert H. Smith School of Business; HENRI SERVAES, London Business School Organized by: PETER TUFANO, Harvard Business School As teachers, we often face the challenge of helping MBA students understand important managerial issues where research is unsettled. This is one of a series of pieces written as a quick introduction to a controversial area which many of us have to touch upon in our courses: the diversification discount. Belen Villalonga has written an overview of some of the issues surrounding the diversification discount and assembled a selected bibliography on the topic. We then invited a small number of leading researchers and teachers in the field to comment on how they treat this topic in the classroom.
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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.193 | 0.115 |
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".