The Canadian Sports Pool and a New Name, Dr Z, 1982
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In my teaching at UBC I had some flexibility in the PhD class. I normally taught the PhD sequence on nonlinear programming and portfolio theory in the fall and then applied stochastic programming and asset-liability management in the winter. Over the years I had between 4–8 students and it was a great class to teach. My 1974 class started the stochastic programming asset-liability modeling work and had Jerry Kallberg and Martin Kusy both of whom wrote major papers with me. There were also other good students. Once in a while the PhD course could be in speculative investments and there I could cover the mathematics of that such as the nice book of Epstein (1977, 2012) plus the applications. Also I could go into interesting topics like blackjack, casino gambling, horse racing, lotteries, etc. The BC Lottery Commission had me as a consultant and that was a lot of fun plus a bit of extra income which was useful for a low paid professor in an expensive city…
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it