Bibliographic record
Abstract
What makes a biography fascinating? Let us list a few characteristics. The central character should face substantial challenges that resonate with the reader. It will help if at least some of the events and personalities are familiar. The author should express herself well, recording the facts accurately and stringing them together into a coherent story. The style of writing should be brisk, easy to follow, and engaging. But most of all, the author should bring the central character to life, faithfully describing his virtues without neglecting his shortcomings and inviting us to finish the book with a deeper understanding of how the subject came to be the individual he was and how that teaches us more about who we are. Charlotte Jacobs's biography of Henry Kaplan attempts, and largely accomplishes, all of those goals. Readers who knew this multifaceted physician will finish the book with a deeper appreciation of his accomplishments, foibles, and impact on patients and colleagues than they began and those who never met him will learn about both Dr. Kaplan himself and about Hodgkin lymphoma, the intriguing disease he worked so hard to overcome, as they gain a new perspective on the intricate interplay among strong personalities, scientific research, medical care, cancer treatment, and national and international politics that underlies the ongoing efforts to cure cancer, the most difficult of diseases.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".