Bibliographic record
Abstract
Latin and the Romance languages occupy a vast space along at least three dimensions: geographical, temporal, and social. Once the language of a small town on the Tiber River in Latium, Latin was carried far afield with the expansion of Roman power. The Empire reached its greatest extent under the reign of the emperor Trajan (98–117 ce ), at which point it included modern-day Britain, Portugal, Spain, France, Italy, Switzerland, Austria, and the Balkan peninsula, as well as immense territories in the Eastern Mediterranean and beyond, making it by far the largest single state the Western world had ever known. Even those distances are dwarfed by the extent of Western European colonial expansion in the 1500s and 1600s, which brought Spanish to most of Latin America and the Caribbean, Portuguese to Brazil, French to Canada, and all three to their many outposts around the world, where they engendered some robust creoles. On the time dimension, the colloquial speech that underlies the Romance languages was already a constant presence during the seven centuries that saw Rome grow from village to empire – and then their history still has twenty centuries to go. Their uses in society extend to every level and facet of activity from treasured world literature to instant messaging. A truly panoramic account of Romance linguistic history would find few readers and probably no writers. The scope has to be limited somehow. Our decision, which may disappoint some readers, is to cover five languages: French, Italian, Portuguese, Romanian, and Spanish.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.323 | 0.170 |
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".