Bibliographic record
Abstract
The Drunk Gambler Problem A few years ago I was asked to give a keynote lecture on the subject of retirement income planning to a group of financial advisors at an investment conference that was taking place in Las Vegas. I arrived at the conference venue early—as most neurotic speakers do—and while I was waiting to go on stage, I decided to wander around the nearby casino, taking in the sights, sounds, and smells of flashy cocktail waitresses, clanging coins, and musty cigars. Although I'm not a fan of gambling myself, I always enjoy watching others get excited about the mirage of a hot streak before eventually losing. On this particular random walk around the roulette tables, I came across a rather eccentric-looking player smoking a particularly noxious cigar, though seemingly aloof and detached from the action around him. As I approached that particular table, I noticed two odd things about Jorge ; a nickname I gave him. First, Jorge appeared to be using a very primitive gambling strategy. He was sitting in front of a large stack of red $5 chips, and on each spin of the wheel he would place one—and only one—of those $5 chips as a bet on the black portion of the table. For those of you who aren't familiar with roulette, this particular bet would double his money if the spinning ball landed on any one of the 18 black numbers, but it would cost him his bet if the ball came to a halt on any of the 18 red numbers or the occasional 2 green numbers.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.157 | 0.065 |
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".