Bibliographic record
Abstract
This is the first global history of lexicography. There are, I think, two reasons why no such book has been written before. The first reason is that there have been so many lexicographical traditions in the world over the past five thousand years: hundreds if not thousands of languages have been documented in wordlists of some sort, and scores of them have been documented in wordlists so numerous, and often so large, that their individual traditions are almost ungraspable by a single historian. There have been global bibliographies of wordlists since the eighteenth century, and, since at least the time of William Marsden’s Catalogue of Dictionaries (1796), some of these have presented the wordlists of each language in chronological order. Information of historical value is naturally present in such bibliographies even when the order is not primarily chronological. In Wolfram Zaunmüller’s Bibliographisches Handbuch der Sprachwörterbücher , the last part of the entry for each language is, where appropriate, an overview of early dictionaries in reverse chronological order, century by century, from the nineteenth as far back as the fifteenth.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.466 | 0.301 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".