Path and rate of development in child heritage speakers: Evidence from Greek subject/object form and placement
Bibliographic record
Abstract
Aims: We investigated: (1) whether differences in accuracy between heritage speakers (HS) and monolingual speakers (MS) signal differences in the path or merely in the rate of language development, and (2) whether, independently of these differences, HS become more accurate as they grow older. Methods: Using an elicitation task, we collected data from three groups of speakers of Greek: HS in the United States and Canada (78–226 months), MS of the same age (77–177 months), and younger MS (42–69 months). In terms of structures, we focused on two phenomena that are encoded differently in Greek and English: subject/object form in reference maintenance contexts and subject placement in embedded wh-dependencies. Data and Analysis: Data were analyzed with mixed-effects logistic regression models. Findings: We found that the heritage group had a lower accuracy and produced different error patterns than both monolingual groups. Specifically, only the heritage group produced non-felicitous lexical subjects/objects in reference maintenance contexts and ungrammatical preverbal subjects in embedded wh-structures. Accuracy, though, increased with age. Furthermore, current amount of heritage language (HL) input and generation, which were included as covariates, emerged as significant predictors in some or all of the conditions. Originality: The inclusion of a younger monolingual group helped us determine whether the different patterns observed in the language of HS are also attested in the language of MS at earlier developmental stages. The inclusion of a wide age range helped us determine whether, independently of differences in the path/rate of development, HS become more accurate as they grow older and accumulate the necessary amount of HL input. Implications: HS may go through developmental stages not attested in L1 acquisition. However, differences in developmental stages do not necessarily entail differences in the outcome of language acquisition. HS’ accuracy may continue to increase, provided that they continue using their HL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".