Bibliographic record
Abstract
THE SHORTEST LITERARY WORKS The author of War and Peace also mastered short forms, including moral tales and the shortest of all literary forms, the quotation. Tolstoy loved quotations. By “quotations” I mean not any set of cited words, but the sort of memorable short saying that we find in Bartlett's Familiar Quotations and similar volumes. Although it is often assumed that Bartlett invented the anthology of quotations, it derives from a tradition extending back to the Renaissance (Erasmus' Adagia ), medieval florilegia, ancient classics including Diogenes Laertius, and the biblical Book of Proverbs, which is itself a collection of collections of Middle Eastern proverbs. To be a quotation in this sense, a set of words must be able to stand on its own as a complete, if brief, literary work. It must be quotable . We may therefore distinguish what I shall call a quotation – a short literary work – from an extract, in the sense of any set of cited words. Extracts, such as the sort of citations footnoted in scholarly articles, are neither offered nor taken as complete works capable of standing on their own. Clearly, not all extracts are quotations. But neither are all quotations extracts. For one thing, although a quotation may have an extract as a source, it may and often does differ from its source if for no other reason than to stand on its own. Becoming a quotation is a change in status that often involves a change in text.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".