Bibliographic record
Abstract
BackgroundThe Romance languages are the direct offspring of spoken Latin, called Vulgar Latin, "the Latin spoken by the common people."In territories that achieved nationhood at some point after the demise of the Roman Empire, some of the vulgar tongues evolved into national languages, not because they were forged as such, but because they came to have autonomous status in the communal consciousness of various emerging societies in the medieval period, as a result of political, social, and other nonlinguistic factors.Once established, national languages such as Italian, French, Spanish, and Portuguese started to undergo change on their own.Without doubt, it was the medium of the written word that came to have the greatest influence on their development over the course of the centuries.Emulation of the great writers has, in fact, always been the greatest factor in shaping their evolution, leading to "trends" in style and vocabulary that have been amply documented and discussed in the vast literature in Romance linguistics.The emergence and evolution of standard Italian is a case-in-point.Pinpointing exactly when the Italian volgare, as distinct from Latin, emerged on the Italian peninsula is impossible.The only thing available to the historical linguist are inscriptions and ancient texts which contain forms that can be identified as being more "Italian" than they are "Latin."The first text to show a conscious use of the volgare is a four-line ninth century riddle written by a Veronese scribe, and hence called the Indovinello veronese.But it was not the Veronese volgare that became the basis of modern-day Italian; rather, it was another volgare, spoken in Tuscany, not for any intrinsic linguistic merit it was perceived to have, but because it was the language made famous by three great Tuscan writers,
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".