The scientist's summer reading list <b>Underland: A Deep Time Journey,</b> <i>Robert Macfarlane,</i> Norton, 2019. 496 pp. <b>Archaeology from Space: How the Future Shapes Our Past,</b> <i>Sarah Parcak,</i> Henry Holt, 2019. 288 pp. <b>Digital Cash: The Unknown History of the Anarchists, Utopians, and Technologists Who Created Cryptocurrency,</b> <i>Finn Brunton,</i> Princeton University Press, 2019. 266 pp. <b>Slime: How Algae Created Us, Plague Us, and Just Might Save Us,</b> <i>Ruth Kassinger,</i> Houghton Mifflin Harcourt, 2019. 318 pp. <b>Because Internet: Understanding the New Rules of Language,</b> <i>Gretchen McCulloch,</i> Riverhead Books, 2019. 336 pp. <b>Moonbound: Apollo 11 and the Dream of Spaceflight,</b> <i>Jonathan Fetter-Vorm,</i> Hill and Wang, 2019. 256 pp. <b>The Fate of Food: What We'll Eat in a Bigger, Hotter, Smarter World,</b> <i>Amanda Little,</i> Harmony, 2019. 350 pp. <b>Fall; or, Dodge in Hell,</b> <i>Neal Stephenson,</i> William Morrow, 2019. 890 pp.
Bibliographic record
Abstract
How will we eat in a warming world? What makes money real? Are we being good ancestors? From a graphic celebration of the semicentennial of the Apollo 11 mission to a dystopian foray into the digital afterlife, this year's summer reading picks—reviewed by an enthusiastic group of early-career scholars—aim to unpack where we came from and where we're headed. Focus on the big picture with an engaging exploration of space archaeology, dig into the details with a thought-provoking ode to algae, or sit back and LOL at an entertaining introduction to internet linguistics.
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 | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.339 | 0.336 |
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".