Heritage Tagalog Phonology and a Variationist Framework of Language Contact
Bibliographic record
Abstract
Heritage language variation and change provides an opportunity to examine the interplay of contact-induced and language-internal effects while extending the variationist framework beyond monolingual speakers and majority languages. Using data from the Heritage Language Variation and Change in Toronto Project, we illustrate this with a case study of Tagalog (r), which varies between tap, trill, and approximant variants. Nearly 3000 tokens of (r)-containing words were extracted from a corpus of spontaneous speech of 23 heritage speakers in Toronto and 9 homeland speakers in Manila. Intergenerational and intergroup analyses were conducted using mixed-effects modeling. Results showed greater use of the approximant among second-generation (GEN2) heritage speakers and those that self-report using English more. In addition, the distributional patterns remain robust and the approximant appears in more contexts. We argue that these patterns reflect an interplay between internal and external processes of change. We situate these findings within a framework for distinguishing sources of variation in heritage languages: internal change, identity marking and transfer from the dominant language.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".