What Heritage Language Speakers Tell Us about Language Variation and Change
Bibliographic record
Abstract
This chapter responds to the questions raised in Chapter 1. It reiterates the need for variationist sociolinguistic analysis of heritage languages to increase our understanding of linguistic structures, variation, and change in multilingual contexts. Each variable is considered through the lens of the profiles corresponding to different sources of change. This allows us to consider whether certain profiles are more common for certain types of variables and of language (types), and whether covariation is more prevalent among any subset of variables. We reiterate how these analyses, based on spontaneous speech in an ecologically valid environment, give a picture of heritage language speakers that contrasts with what we have learned from experimental/psycholinguistic studies, highlighting their stability and consistency with homeland varieties in most cases. Suggestions are made for how this approach can be extended to other under-documented, endangered, and smaller languages, along with discussion of benefits of the HLVC methodology to community members, educators and students, and the field of linguistics. The chapter concludes by reporting on students’ positive responses to engagement with the project.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".