Our autumn reading list <b>The Nature of Life and Death: Every Body Leaves a Trace,</b> <i>Patricia Wiltshire,</i> G. P. Putnam's Sons, 2019. 304 pp. <b>Why Trust Science?,</b> <i>Naomi Oreskes,</i> Princeton University Press, 2019. 374 pp. <b>The Republic of Color: Science, Perception, and the Making of Modern America,</b> <i>Michael Rossi,</i> University of Chicago Press, 2019. 330 pp. <b>Superheavy: Making and Breaking the Periodic Table,</b> <i>Kit Chapman,</i> Bloomsbury Sigma, 2019. 304 pp. <b>Higher and Colder: A History of Extreme Physiology and Exploration,</b> <i>Vanessa Heggie,</i> University of Chicago Press, 2019. 264 pp. <b>Something Deeply Hidden: Quantum Worlds and the Emergence of Spacetime,</b> <i>Sean Carroll,</i> Dutton, 2019. 362 pp. <b>How To: Absurd Scientific Advice for Common Real-World Problems,</b> <i>Randall Munroe,</i> Riverhead Books, 2019. 320 pp. <b>Meat Planet: Artificial Flesh and the Future of Food,</b> <i>Benjamin Aldes Wurgaft,</i> University of California Press, 2019. 264 pp. <b>Fashionopolis: The Price of Fast Fashion and the Future of Clothes,</b> <i>Dana Thomas,</i> Penguin Press, 2019. 318 pp.
Bibliographic record
Abstract
What can a lowly lichen reveal about a grisly murder case? Which common clothing item requires 5000 gallons of water to create? Where is the best place for a pilot to crash a malfunctioning airplane? Chockful of interesting trivia and thoughtful scholarship, the books on this year's fall reading list—reviewed by alumni of the AAAS Science and Technology Policy Fellowship program—tackle topics ranging from the future of food to the nature of reality. Consider a careful analysis of why we ought to trust science or join a harrowing expedition to the most extreme environments on Earth. Dive into a fascinating quest to identify new elements or crack open an eye-opening history of color science.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.621 | 0.576 |
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".