Null Subjects in Heritage Languages: Contact Effects in a Cross-linguistic Context
Bibliographic record
Abstract
This paper presents an overview of the first variable examined in the Heritage Language Variation and Change in Toronto project (Nagy 2009), which strives to apply consistent methodology across multiple language-contact contexts and variables to advance our understanding of contact-induced change. It is principally comprised of sociolinguistic interviews conducted in Toronto with 40 speakers from each of six heritage languages (Cantonese, Faetar, Italian, Korean, Russian and Ukrainian). Participants are also asked about their ethnic identification, language use, and linguistic attitudes (Keefe & Padilla 1987, Hoffman & Walker 2010). Responses are translated into index scores to quantify each speakers' orientation toward their heritage language/culture and their English/"Canadian" culture.\nHere we examine the effects of a constellation of factors (linguistic, typological, demographic, social) on a single linguistic variable: (pro-drop). Our Cantonese, Italian and Russian data, ~6,000 tokens, is contrasted with a sample from the Toronto English Archive (Tagliamonte & Denis 2010). For comparability with previous studies of pro-drop, we examine the effects of continuity of reference (Cameron 1995), contextual/formal ambiguity of the subject's referent (Paredes Silva 1993), clause type (Harvie 1998), priming by the preceding subject (Torres Cacoullos & Travis 2010), person and number of the subject, and tense of the following verb. Pro-drop rates and constraint hierarchies in each HL show no relationship to any indices of generation since immigration, ethnic identity or language use, suggesting that this variable is not used to construct ethnic identity and is not undergoing change as the heritage varieties of each language develop in Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".