Adaptation of verbs borrowed from Spanish in the speech of descendants of Ukrainian immigrants living in South America
Bibliographic record
Abstract
The paper discusses models and strategies for the adaptation of verbs borrowed from Spanish into the migrant dialects of the Ukrainian language spoken in South American countries. There are models in which the Spanish verb is borrowed with the Spanish infinitive ending and without it. The reasons behind choosing one of the models in the speech of descendants of Ukrainian immigrants are investigated. It is stated that one must take into account a set of criteria that affect this process (the frequency of lexemes, their semantics, the number of syllables, etc.). The paper identifies new types of adaptation of borrowed verbs that have not been previously discussed in linguistic publications. The cases of bare use of verbal lexemes in the speech of informants are also analyzed. Examples of the aspect pairs from borrowed Spanish verbs are discussed. This category is in the process of grammaticalization. In some cases, when forming an aspect pair with a prefix, informants apply prefixes inherent to the respective verbal prototypes in their native dialect. The copying of verb control from both Ukrainian and Spanish when using a borrowed verb is also attested. Using the example of the Ukrainian-Spanish bilingualism in the migrant community living in South American countries for a little more than a century, we have the opportunity to observe the changes that occur in the Slavic verbal system under the influence of the dominant Romance language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".