Bibliographic record
Abstract
When The Emperor of All Maladies was published in late 2010, I knew it would be near the top of my stack of books to read. Since I am a PhD student in the History of Science and Medicine, reading a notable book on the history of cancer and its treatments is a must. Sadly, at the time of its publication, my mother had just died unexpectedly at age 82 of a disease for which she had never received a prior diagnosis: cancer, or acute myelogenous leukemia, to be exact. From diagnosis to death took a mere six days. So I hesitated to take this in-depth look at cancer, a disease that left my family and me stunned and grieving from such a sudden loss. I reasoned, however, that the approach of the book would be detached and scientific, perhaps similar to the tone of the academic tomes that I tackle each week. I began to read.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".