Bibliographic record
Abstract
few years ago, just as I was about to introduce binomial probabilities in my precalculus class, the Edmonton Oilers were in a first-round play-off series with the Dallas Stars. Each team had won a game. The series suggested a problem: given that the Oilers had a probability p of winning any game, what was the probability that they would win the series? I focus on the Oilers because the small university where I teach is located a one-hour drive from their home in Edmonton. Our initial figure of p = .3 was based loosely on the Oilers' record against the Stars. We began with what I will call the brute-force method, treating the rest of the series as a five-game series. After completing the brute-force solution, we searched for a shorter, more elegant, solution. Although the solutions that we unearthed along our path of discovery are not new, they illustrate beautifully the process by which many mathematical problems are solved, extended, and generalized.
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 | 0.007 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".