Bibliographic record
Abstract
Epilepsy Bibliography: Books and Monographs (1945–2003). 8th ed. Y. Fukuyama, Kyowa, Tokyo, 2004, 234pp. This is a monograph about monographs, specifically those written about epilepsy between 1945 and 2003. It is the eighth edition, since the work first appeared in 1975. It certainly takes a degree of resourcefulness, some would say compulsiveness, to compile this list of 1,349 books and monographs, categorized according to such themes as basic neuroscience, pediatrics, mental aspects, and diagnostics (EEG atlases were not included). If you wish to know who has written/edited the largest number of books in the past 50 years, you will find the information here.* Of interest is a graph in the preface that illustrates the number of books and monographs about epilepsy produced each year, starting with an average of six per year in the 1950s and then doubling each decade to ∼50 per year in the 1980s and 1990s. Although the author acknowledges that the list may not be comprehensive, I found that only one of five randomly selected obscure monographs from my bookshelf was not included. This is a handy complement to the standard literature review done through PubMed and similar archives that generally do not incorporate the type of material listed in this book. A CD with the contents presented as a searchable bibliography is included. The author has also generously allowed the material to be made available for online access on the ILAE Web site. *That honor goes to J. Kiffin Penry, with 28 books under his name.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.246 |
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".