Switching the majority language: The case of heritage Greek in North and South America
Bibliographic record
Abstract
Abstract This study aims to understand how cross-linguistic influence (CLI) and heritage language (HL) use influence children’s HL acquisition of vocabulary, reference, and word order. To this end, we compared elicited production data collected from two groups of child heritage speakers: a group of Greek-English bilingual children (Mean Age: 10;11) residing in North America and a group of Greek-Spanish bilingual children (Mean Age: 10;09) residing in South America. Because Greek is closer to Spanish than to English in all three domains of interest, the ‘Greek-English’ and ‘Greek-Spanish’ dyads are ideal for the study of CLI and its role on HL acquisition. Regression analyses revealed that the South American group outperformed the North American group, despite receiving an overall lower amount of Greek input. Thus, above and beyond input, the typological proximity with the ML may boost children’s HL performance across domains of HL development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".