The semantics of the BCS and Bulgarian motion verbs ‘doći’ and ‘dojda’ - ‘to come, arrive’: a contrastive corpus-based study
Bibliographic record
Abstract
The verbs ‘doći’ in Bosnian, Croatian, Serbian (= BCS) and ‘dojda’ in Bulgarian (= Blg.) are among the most frequent motion verbs. They are used in both concrete contexts (of human motion and motion of various objects) and metaphorical contexts in which features of concrete motion are transferred into abstract domains. A semantic examination of such verbs may reveal tendencies related to universal and language-specific meaning extensions of motion verbs. Our contrastive semantic study is based on a small parallel corpus of BCS literary texts and their Blg. translations. We examine contexts in which BCS doći relates to Bulgarian ‘dojda’, and those in which BCS ‘doći’ relates to other Bulgarian verbs or phrases (implying either spatial notions such as ‘go out’, ‘go down’, ‘return’, ‘come nearer’, and ‘appear’, or some non-spatial notions such as ‘take a deep breath’, ‘come to one’s senses’, etc.). Our questions are: What are the differences in the semantic networks of these two seemingly very similar verbs in very closely related languages? In which situations with concrete and abstract motion are ‘doći’ and ‘dojda’ “perfect matches”, and in which ones are they less perfect matches? Which metaphorical extensions are common, and which are limited to one language only, and how can this be explained? What facts about verbal (near-)synonymy does a parallel corpus study reveal?
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".