Bibliographic record
Abstract
By its very nature, a discursive reminiscence has to be somewhat self-referential. Furthermore, it cannot offer an exhaustive survey of the many special topics to which it may refer. Rather than suffer “strangulation by footnotes and references, ” for brevity and equity I do not provide references to subjects such as auctions, the new industrial organization, or experimental gaming because there are more than adequate references and survey articles available. When I first skimmed The Theory of Games and Economic Behavior in 1948, I did not really understand it, but I sensed that this was the way to go in the study of multiperson conscious strategic behavior. I had heard a little about operational research casually in 1944 in two applications: one concerning how to aim an anti-aircraft gun to take account of the plane’s motions during the time it took to reach it after firing, and I had a vague idea that one could try to analyze the best ways for convoy defense by using some form of mathematics. The visit to the main library at the University of Toronto to look randomly at the new books in economics led to my going to Princeton to study game theory. To this day, Ihave been struck with the thought that it is possible to not know precisely what one is looking for, but recognize immediately when one finds it. At Princeton, there was some direct talk about operations research per se, and only a few of us were aware of the newly formed Operations Research Society. But in a few years around Fine Hall (and elsewhere), much of the mathematics relevant to its development was being developed. Among the visitors, students, and faculty were Bellman,
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.212 | 0.122 |
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".