Bibliographic record
Abstract
Dear Readers,When I spotted Alexandra Alter’s article “James Patterson Has a Big Plan for Small Books” in The New York Times on March 21, 2016, I immediately thought the story was about a new innovation that Patterson had introduced for small format children’s books. Instead, the article describes Patterson’s new line of short novels aptly named BookShots that will include thrillers, mysteries, romances, science fiction, and (eventually) nonfiction. While most people recognize Patterson’s name for his prodigious output of thrillers, he is also known for publishing nearly 50 children’s books, which have sold more than 36 million copies worldwide. He has also written popular mysteries, romances, and young adult novels, but he now has plans to write for adult readers who don’t normally make time for reading. Indeed, the BookShots home page advises prospective customers that “Life moves fast—books should too”.While I have no objection to Patterson’s new line of short, cheaply produced books that may eventually be stocked next to magazines and candies in grocery stores, I do hope that publishers of children's books will embrace an opposite trend by publishing longer books for young readers who do have time to read. Let’s not assume that all children are abandoning reading for movies, television, video games, and social networking.The strengths of Patterson’s new books are their lively, incisive writing, and of course, engaging plots that pack a great deal into few words. Brevity will certainly lend Patterson’s new books a narrative crispness that will appeal to readers who may already enjoy reading digital content on their mobile devices. There is nothing wrong with having an appetite for short fiction, but young readers will surely benefit from having access to books that encourage deeper, slow reading.Our summer issue is filled with recommended books that can be read deeply and re-read, so let’s encourage young readers to take time to more fully comprehend and appreciate words, ideas, and stories.Happy reading!Robert Desmarais Managing Editor
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.469 | 0.521 |
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".