Bilingual word familiarity in Cantonese and English
Bibliographic record
Abstract
Bilingual speakers are typically unbalanced in their vocabularies in each language, with each language’s lexicon being representative of the experiences and domains in which the bilingual uses that language. This can create a challenge in creating word lists for speech experiments, as the frequency counts from publicly available corpora do not represent the appropriate vocabulary domains. In this paper, we report on a word familiarity rating task in Cantonese and English that was designed to pretest stimuli in both languages to confirm the items were matched in familiarity for use in subsequent speech tasks. Participants fell into four different groups: Cantonese-English bilinguals who grew up in a Cantonese-dominant location, Cantonese-English bilinguals who grew up in an English-dominant location, North American English speakers with no Chinese language experience, and international English speakers with no Chinese language experience. All listeners were presented with blocks of Cantonese words/nonwords and English words/nonwords and were asked to rate the familiarity of each item. We compare these ratings across participant groups and in relation to corpora for each language to assess how the listener groups perform. We discuss these results in the context of designing stimuli lists that are appropriate for diverse, multilingual populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".