THE USE OF GENERIC SUBJECTS BY ROMANIAN HERITAGE LANGUAGE SPEAKERS IN MULTILINGUAL CONTEXTS
Bibliographic record
Abstract
This study examines the use of plural nouns in generic contexts among heritage speakers of Romanian growing up in an English-dominant context in Canada and acquiring French as a third language through the French Immersion program. Building on crosslinguistic research on genericity, we investigate whether these children produce target-like plural noun forms in Romanian compared to English and French, and whether their performance reflects crosslinguistic influence or factors tied to heritage language maintenance. Sixteen heritage Romanian children and five Romanian-dominant controls completed elicitation tasks in all three languages, testing both generic and specific contexts. Statistical analyses revealed significant differences across languages, with Romanian showing the lowest accuracy, particularly in generic contexts, and English showing near-target performance. These results provide new empirical evidence from a rarely studied population, highlighting the vulnerability of heritage morphosyntax to dominant-language structural patterns. The findings underscore the importance of heritage language input and the potential for typologically related languages, such as French, to support heritage language maintenance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".