Processing costs in Cantonese-Latin script-mixing
Bibliographic record
Abstract
Abstract An emerging trend among young Cantonese speakers is to script-mix morphographic Chinese characters with Latin graphemes in social media exchanges, uncommon in traditional Chinese contexts. Results of a self-paced reading experiment with Cantonese speakers are reported to determine whether script-mixing incurs processing costs, and if so, whether these can be attributed to Inhibitory Control of one of the two scripts or to Dual Activation of both scripts but with slower lexical access within the non-dominant script. Sentences were presented either entirely in Chinese characters or had one region presented in Latin graphemes. Processing costs arose only at the switch from Latin graphemes back to Chinese characters, pointing to the involvement of Inhibitory Control. Further, these costs only appeared in a subset of grammatical categories, potentially coinciding with parsing uncertainties. As such, a combination of script-mixing and parsing complexities could be seen to result in processing costs in certain sentential positions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".