Summer reading 2021 <b>Solving Public Problems: A Practical Guide to Fix Our Government and Change Our World</b> , <i>Beth Simone Noveck</i> , Yale University Press, 2021, 448 pp. <b>Technically Food: Inside Silicon Valley's Mission to Change What We Eat</b> , <i>Larissa Zimberoff</i> , Abrams Press, 2021, 240 pp. <b>Unwell Women: Misdiagnosis and Myth in a Man-Made World</b> , <i>Elinor Cleghorn, Dutton</i> , 2021, 400 pp. <b>The Uncommon Knowledge of Elinor Ostrom: Essential Lessons for Collective Action</b> , <i>Erik Nordman</i> , Island Press, 2021, 256 pp. <b>The Ascent of Information: Books, Bits, Genes, Machines, and Life's Unending Algorithm</b> , <i>Caleb Scharf</i> , Riverhead Books, 2021, 352 pp. <b>A Quantum Life: My Unlikely Journey from the Street to the Stars</b> , <i>Hakeem Oluseyi and Joshua Horwitz</i> , Ballantine Books, 2021, 368 pp. <b>Blue: In Search of Nature's Rarest Color</b> , <i>Kai Kupferschmidt</i> , The Experiment, 2021, 224 pp. <b>The Memory Thief and the Secrets Behind How We Remember: A Medical Mystery</b> , <i>Lauren Aguirre</i> , Pegasus Books, 2021, 336 pp.
Bibliographic record
Abstract
A journalist probes the tech companies racing to entice consumers—and investors—with futuristic foods. An outsider documents his ascent in academia. A policy expert proposes a human-centered approach to solving society's problems. From an ode to azure to a deep dive into data, this year's summer reading picks—reviewed by alumni of the AAAS Mass Media Science & Engineering Fellows program—offer readers fresh perspectives on timely scientific topics. Confront the biases that have long imperiled women's health, probe the mysteries of memory, celebrate a prescient economist, and more, with the books reviewed below.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.318 | 0.228 |
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".