Bibliographic record
Abstract
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 to this and other journals read it and took it to heart, how much more readable and enjoyable even the most arid scientific papers would be!I read Eats, Shoots & Leaves as a writer who is constantly struggling to improve his style and as an editor who is compelled to improve that of his contributors.(And, as I wrote that, I asked myself ''Should there be a comma before the 'and'''?)This book is a cri de coeur of a ''writer and journalist who started out as a literary editor with a blue pencil, and then got sidetracked''.Truss is a self admitted stickler.She hectors and mocks but does so with such a relatively light touch that even the most careless writer is unlikely to be offended.Her mission is, nevertheless, a serious one: she is determined to improve writing by focusing attention on the most neglected element in the holy trinity of style-punctuation (the others are structure and words).How can you not chuckle when someone stakes out her position as follows: ''In some matters of punctuation there are simple rights and wrongs; in others, one must apply a good ear to good sense… Eats, Shoots & Leaves is not a book about grammar.I'm not a grammarian.To me a subordinate clause will forever be… one of Santa's little helpers.A degree in English language is not a prerequisite for caring about where a bracket is preferred to a dash, or, needs to be replaced by a semicolon.If I did not believe that everyone is capable of understanding where an apostrophe goes, I would not be writing this book.''In the Introduction punctuation is defined and described in various ways.One is by
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 | 0.000 | 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".