Variation in Path Encoding in Motion Events in Toronto Heritage Cantonese
Bibliographic record
Abstract
This study examines path encoding in motion event expression in Toronto Heritage Cantonese using a variationist sociolinguistic methodology informed by studies on the typology of motion events (Talmy 2000). Cantonese inherently exhibits some variability in that both satellite-framing and verb-framing strategies of path encoding are grammatical and natural (Yiu 2014). Work on motion events in bilinguals suggest that typologically different languages may have crosslinguistic effects on motion event expression (Filipović 2011, Brown and Gullberg 2008, Wang and Wei 2019, among others). In light of this body of work, I investigate the linguistic and social factors that are relevant to the variation seen in Toronto Heritage Cantonese and Hong Kong Cantonese, the homeland variety. Spontaneous speech from 23 sociolinguistic interviews from the Heritage Language Documentation Corpus (Nagy 2011) is analyzed by extracting all relevant examples of motion event expression (n = 1991). Intergenerational and diatopic comparisons are made using comparative variationist methods. The results suggest stable variation in the homeland speakers, but change among the heritage speakers, which cannot be attributed to simplification or contact with English.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".