Bibliographic record
Abstract
The author addresses Slovene-English language contact, both in the immigrant context and in Slovenia. The direct contact of Slovene and English in the case of Slovene Americans and Canadians is examined from two perspectives: social and cultural on the one hand and linguistic on the other. In the first part, I present the general linguistic situation in Cleveland (and to a minor extent in Washington, D.C. and Toronto), with emphasis on language maintenance and shift, the relationship between mother tongue preservation and ethnic awareness, and the impact of extralinguistic factors on selected aspects of the linguistic behavior of the participants in the study. I then compare the use of second person pronouns as terms of address and the use of speech acts such as compliments to determine the role of different cultural backgrounds in the speakers' linguistic choices. The linguistic part of the analysis focuses on borrowing and code switching, as well as on the influence of English on seemingly monolingual Slovene discourse, where the Slovene inflectional system in particular is being increasingly generalized, simplified and reduced, and Slovene word order is beginning to resemble that of English. Finally, the rapidly growing impact of English on Slovene in Slovenia on various linguistic levels from vocabulary to syntax and intercultural communication is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".