What Are Heritage Languages and Why Should We Study Them?
Bibliographic record
Abstract
This chapter defines heritage languages and motivates their study to understand linguistic diversity, language acquisition and variationist sociolinguistics. It outlines the goals of Heritage Language Variation and Change in Toronto (HLVC), the first project investigating variation in many heritage languages, unifying methods to describe the languages and push variationist sociolinguistic research beyond its monolingually oriented core and majority-language focus. It shows how this promotes heritage language vitality through research, training, and dissemination. It lays out overarching research questions that motivate the project: Do variation and change operate the same way in heritage and majority languages? How do we distinguish contact-induced variation, identity-related variation, and internal change? Do heritage varieties continue to evolve? Do they evolve in parallel with their homeland variety? When does a heritage variety acquire its own name? What features and structures are malleable? How consistent are patterns across languages? Are some speakers more innovative? Can attitudes affect ethnolinguistic vitality? How can we compare language usage rates among communities and among speakers?
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".