Second person pronouns used by Slovene and American Slovene speakers as linguistic markers of personal and social (in)equality
Bibliographic record
Abstract
This paper discusses the use of second person pronouns in Slovene as linguistic markers of personal and social (in)equality in face-to-face interaction. In addition to the fundamental social dimensions of power/status and solidarity that are usually associated with the choice of a particular pronoun in such interactions, I explore some other dimensions such as formality and casualness that may also contribute to the choice. The focus is on the comparison of the use of Slovene second person pronouns in their native and diaspora contexts. While the rules for their use in Slovenia are relatively well established and observed in a fairly consistent manner, especially by older speakers, their use in the North American context is quite different. The questionnaire responses by Slovenes and their descendants living in the United States and Canada show that these pronouns are often used almost as if at random and that, especially with younger speakers, the predorninant form has become "ti". It is possible that this is due to the dirninishing knowledge of Slovene and the speakers' uncertainty as to which form to use, but also to the very strong influence of English with its exclusive use of you. The growing tendency of younger speakers in Slovenia toward ti is also addressed as a possible indication of a language change under way.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".