Bibliographic record
Abstract
Edited by Lynne Truss. (Pp 209; hardback.) Gotham Books (Penguin Group) New York, 2003, ISBN 1-592-40087-6. Most writers punctuate. Not everyone punctuates correctly. Few punctuate well. Lynne Truss’s best-seller, Eats, Shoots & Leaves is certain to raise each reader’s writing one large notch. As a bonus, it is a great read; witty, caustic, and wise. If only I had written this review before the book’s meteoric climb to the top best-seller lists (where it remains). Had I done so this would not now be in the company of the hundreds of other reviews, mostly positive, ranging from warm praise to exuberance. Only a few—notably that by Louis Menand in the New Yorker (June 28, 2004, pp 102–4)—are profoundly critical. (Methinks Menand must be a sourpuss.) This is a book to be read for fun and profit. If all contributors …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.160 | 0.160 |
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".