Veni, Vidi, Vici, Dixi: Latin as the Dominant Language in the Roman Near East
Bibliographic record
Abstract
Syria becoming a consular province by 58 BCE marked the beginning of the Roman period of the Near East, and as a result, Latin was introduced as the language of the East’s rulers to a linguistic landscape dominated by native Aramaic and Greek as a lingua franca. However, scholarly research has focused more on Greek’s relationship with Latin and Aramaic than Latin and Aramaic’s relationship with each other. Latin was never enforced or widely spoken in the Near East compared to Aramaic or Greek, yet that does not mean Latin had no impact on the region. This study analyzes bilingual Latin and Aramaic inscriptions in the Near East using sociolinguistic theory surrounding language contact and dominance, revealing meaningful language contact between Latin and Aramaic speakers. The presence of Aramaic-Latin bilingualism, Latin’s higher prestige, and the influence of the Roman army on the Aramaic lexicon in light of linguistic theory asserts that Latin impacted the Near East at a linguistic level due to Latin’s role as the dominant language despite Latin initially appearing to be uninfluential in the Near East. By positioning Latin as the dominant language in relation to Aramaic, this research challenges the notion of a lack of meaningful language contact between these groups, and aims to encourage further research into lesser-discussed linguistic dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".