Bibliographic record
Abstract
The study investigated the experience of language brokering in highly proficient Chinese-English bilinguals where 50% of them were language brokers. Essentially, language brokering as a phenomenon is the activity of informal translation to facilitate communication between persons and languages. Predominantly from first- and second-generation immigrant households, brokers take on many different settings and materials to achieve these informal translation activities and tasks. It has been shown in previous studies, that brokers tend to be more agile and dynamic across languages. The present study used counterpart idioms to test language brokers and non-brokers recording their accuracy. The counterpart idiom judgment task tested idiom type (decomposability and familiarity) in both English and Chinese language directions [e.g., English direction “kill two birds with one stone”, answer “一石二鸟”- pin yin: yī shí èr niǎo (figurative meaning in both English and Chinese – “to succeed in achieving two things in a single action”)]. Findings showed that brokers similarly scored in decomposable idioms (D) and non-decomposable (ND) particularly in Chinese. Non-brokers showed significantly differently in both decomposable (D) and non-decomposable (ND) idioms where decomposable idioms (D) scored greater than non-decomposable (ND) in Chinese. Both groups responded more accurately in D than ND in English. Overall, brokers had a higher accuracy than non-brokers and responded more similarly across languages, idiom types (decomposable and non-decomposable), and familiar and unfamiliar idioms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".