Bibliographic record
Abstract
I believe that almost everyone who uses the book finds it more convenient to have recourse to the Index first. (John Roget, Introduction to Roget's Thesaurus (1879, 2nd edn), cited by Roget (2002), Introduction, p. xv ) Dear Alexander Valkner , … it was a relief to come across your long, brilliant piece in a recent issue of Comment , namely: The History of Dictionaries. … From intimacy you travelled to grandeur, then back and forth, like a marvellously controlled metronome. I admired the way your essay builds on itself so meticulously, and the way it is anecdotal, accessible, and, finally, shading toward the confessional. I recognized only too well the moment in which you were tempted to approach some of our great writers to see whether or not they ‘indulge’, keeping a thesaurus hidden in their desk drawer. (Reta Winters, in Shields (2002), 163–4) When it's ajar … Almost every publication on Latin literature today practises citation from Isidore. Through the twentieth century, this was a matter of itemic consultation through a modern Index. Until 1991, the closest that many, perhaps most, scholars ever came to reading Isidore's magnum opus was, for sure, the Index verborum of Wallace Lindsay's OCT (1911a), vol. ii , 371–442: Latin, and 443: Greek (with ibid ., 444–50: Loci citati ). Then, at a stroke, the publication of Robert Maltby's invaluable Lexicon of Ancient Latin Etymologies (1991) finessed this reflex from the Latinist's apparatus of automatic procedures: ‘[M.] has assembled all the explicitly attested etymologies of Latin antiquity, from the predecessors of Varro to Isidore of Seville; he has covered glossaries and scholia as well as the standard ancient etymological source-works.’
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".