Heritage Language Variation and Change – How Complex Is It?
Bibliographic record
Abstract
Abstract We focus on complexity from the comparative variationist perspective, a sociolinguistic approach that examines variable aspects of language (that is, different ways of saying the same thing). Arguably, variable elements are harder to acquire than categorical ones, as a Variability Matrix must be acquired along with every element. This matrix contains probabilistic information about when each form is (more) appropriate, according to an array of factors. These include inter-speaker (social) and intra-speaker (linguistic context) predictors. We ask how the Variability Matrix for predictors of a variable compares between heritage speakers (people living in a context where their language is a minority language) and homeland speakers (people living in a context where their language is a majority language), and how these can fairly be compared. In the variationist approach, multivariate regression analyses reveal the predictors (and levels within each predictor) of a response or dependent variable and their corresponding Variability Matrices. However, the variationist field lacks an established comparative methodology to determine how/if varieties differ. One shortcoming is that different-sized samples are often compared, implicating different levels of statistical significance even when the populations’ patterns are identical. Comparison of variable patterns in Heritage and Homeland Cantonese illustrates one solution. We revise analyses conducted previously of two morphosyntactic variables: prodrop and classifiers (Nagy, 2015; Nagy & Lo, 2019) in Cantonese, applying a bootstrap procedure to mitigate issues associated with unequal-sized datasets frequent in studies of minority and endangered varieties. From these analyses, we learn that heritage and homeland grammars’ degrees of complexity are similar: the matrices of (significant) frequencies are the same size. This approach allows us to consider not just which surface forms constitute the heritage vs. homeland varieties, but also the complexity of the decision-making process the speakers apply in selecting among the forms. As one might expect, heritage and homeland speakers are capable of equally complex processes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".