Bibliographic record
Abstract
Inside this book is an older book. In 1993, the MAA published “The Wohascum County Problem Book”, and a few years ago we were asked to consider reissuing that book with a less rustic and more descriptive title. Meanwhile, we had many more problems to contribute, and so the original list of 130 has grown to 208. The new problems are, if anything, more likely to involve pattern finding and experimentation, although technology is generally not needed or even particularly helpful. In difficulty the new problems tend to be in the middle range of the original book, so anyone familiar with that book who looks only at the very beginning or the very end of the problem list may not notice much difference. From a geographical perspective, we haven't tried to move the problems that were originally set in Wohascum County, and we still can't tell you where to look for that setting on a map. We have been asked, and in any case it is appropriate in a preface, to say something about the purpose of this particular collection. There are actually multiple purposes, and different users will no doubt have their own priorities. One purpose is entertainment; we think these problems are attractive and will provide mathematical pleasure to those who spend time with them. This has been confirmed over the years by undergraduates at Carleton and St. Olaf Colleges, where many of the problems were first posed as weekly challenges, by high-school age (but unusually talented and enthusiastic) participants at Canada/USA Mathcamp, and by a variety of others.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.143 | 0.062 |
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".